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Sample records for relationship qsar study

  1. Quantitative structure activity relationship (QSAR) studies on nitazoxanide-based analogues against Clostridium difficile In vitro.

    Science.gov (United States)

    Zhang, Han; Liu, Xiwang; Yang, Yajun; Li, Jianyong

    2016-09-01

    Quantitative structure activity relationship (QSAR) has been established between the various physiochemical parameters of a series of nitazoxanide-based analogues and its antibacterial activity against Clostridium difficile. Genetic function approximation (GFA) and comparative molecular field analysis (CoMFA) techniques were used to identify the descriptors that have influence on biological activity. The most influencing molecular descriptors identified in 2D-QSAR include spatial, topological, and electronic descriptors, while electrostatic and stereoscopic fields were the most influencing molecular descriptors identified in 3D-QSAR. Statistical qualities (r2, q2) indicated the significance and predictability of the developed models. The study indicated that antibacterial activity of Clostridium difficile can be improved by increasing molecular connectivity index, local charge surface index, sharp index and decreasing molecular flexibility index.

  2. Quantitative structure-activity relationship (QSAR) study of a series of benzimidazole derivatives as inhibitors of Saccharomyces cerevisiae.

    Science.gov (United States)

    Podunavac-Kuzmanović, Sonja O; Cvetković, Dragoljub D; Jevrić, Lidija R; Uzelac, Natasa J

    2013-01-01

    A quantitative structure activity relationship (QSAR) has been carried out on a series of benzimidazole derivatives to identify the structural requirements for their inhibitory activity against yeast Saccharomyces cerevisiae. A multiple linear regression (MLR) procedure was used to model the relationships between various physicochemical, steric, electronic, and structural molecular descriptors and antifungal activity of benzimidazole derivatives. The QSAR expressions were generated using a training set of 16 compounds and the predictive ability of the resulting models was evaluated against a test set of 8 compounds. The best QSAR models were further validated by leave one out technique as well as by the calculation of statistical parameters for the established theoretical models. Therefore, satisfactory relationships between antifungal activity and molecular descriptors were found. QSAR analysis reveals that lipophilicity descriptor (logP), dipole moment (DM) and surface area grid (SAG) govern the inhibitory activity of compounds studied against Saccharomyces cerevisiae.

  3. Structure-hepatoprotective activity relationship study of sesquiterpene lactones: A QSAR analysis

    Science.gov (United States)

    Paukku, Yuliya; Rasulev, Bakhtiyor; Syrov, Vladimir; Khushbaktova, Zainab; Leszczynski, Jerzy

    This study has been carried out using quantitative structure-activity relationship analysis (QSAR) for 22 sesquiterpene lactones to correlate and predict their hepatoprotective activity. Sesquiterpenoids, the largest class of terpenoids, are a widespread group of substances occurring in various plant organisms. QSAR analysis was carried out using methods such as genetic algorithm for variables selection among generated and calculated descriptors and multiple linear regression analysis. Quantum-chemical calculations have been performed by density functional theory at B3LYP/6-311G(d, p) level for evaluation of electronic properties using reference geometries optimized by semi-empirical AM1 approach. Three models describing hepatoprotective activity values for series of sesquiterpene lactones are proposed. The obtained models are useful for description of sesquiterpene lactones hepatoprotective activity and can be used to estimate the hepatoprotective activity of new substituted sesquiterpene lactones. The models obtained in our study show not only statistical significance, but also good predictive ability. The estimated predictive ability (rtest2) of these models lies within 0.942-0.969.

  4. A quantitative structure-activity relationship (QSAR) study of some diaryl urea derivatives of B-RAF inhibitors.

    Science.gov (United States)

    Sadeghian-Rizi, Sedighe; Sakhteman, Amirhossein; Hassanzadeh, Farshid

    2016-12-01

    In the current study, both ligand-based molecular docking and receptor-based quantitative structure activity relationships (QSAR) modeling were performed on 35 diaryl urea derivative inhibitors of V600EB-RAF. In this QSAR study, a linear (multiple linear regressions) and a nonlinear (partial least squares least squares support vector machine (PLS-LS-SVM)) were used and compared. The predictive quality of the QSAR models was tested for an external set of 31 compounds, randomly chosen out of 35 compounds. The results revealed the more predictive ability of PLS-LS-SVM in analysis of compounds with urea structure. The selected descriptors indicated that size, degree of branching, aromaticity, and polarizability affected the inhibition activity of these inhibitors. Furthermore, molecular docking was carried out to study the binding mode of the compounds. Docking analysis indicated some essential H-bonding and orientations of the molecules in the active site.

  5. Quantitative structure-activity (affinity) relationship (QSAR) study on protonation and cationization of alpha-amino acids.

    Science.gov (United States)

    Siu, Fung-Ming; Che, Chi-Ming

    2006-11-09

    A quantitative structure-activity (affinity) relationship (QSAR) study is carried out to model the proton, sodium, copper, and silver cation affinities of alpha-amino acids (AA). Stepping multiple linear regression (MLR), partial least squares (PLS), and artificial neural network (ANN) approaches are applied to elucidate the multiple factors affecting these affinities. The MLR and PLS models reveal that the variation in proton affinity is attributed to the highest electrophilic superdelocalizability of nitrogen (major) and the number of rotatable bonds (minor) in AA. The noncovalent interactions, especially ion-dipole interactions, are responsible for the changes in Na+ affinity. The ionization potential, dipole moment of the side chain, and degree of linearity are the properties of AA that give the best correlation with the Cu+ and Ag+ affinities. The ANN models are developed to study the relationships (linear or nonlinear) between the molecular descriptors and binding affinities. The ANN models show higher predictive power. The QSAR models are used to study the binding forms of AA (neutral vs zwitterionic) upon protonation/cationization. To our knowledge, this is the first attempt to carry out a QSAR study on protonated/cationized AlphaAlpha to elucidate their binding properties. In virtue of the Na+ affinity ANN model, the Na+ affinities of dihydroxyphenylalanine (DOPA) were predicted. This work may pave the way for the success of applying similar approaches to peptides or proteins (with AA as the building blocks) in the future.

  6. Synthesis, biological activities, and quantitative structure-activity relationship (QSAR) study of novel camptothecin analogues.

    Science.gov (United States)

    Wu, Dan; Zhang, Shao-Yong; Liu, Ying-Qian; Wu, Xiao-Bing; Zhu, Gao-Xiang; Zhang, Yan; Wei, Wei; Liu, Huan-Xiang; Chen, An-Liang

    2015-05-13

    In continuation of our program aimed at the development of natural product-based pesticidal agents, three series of novel camptothecin derivatives were designed, synthesized, and evaluated for their biological activities against T. Cinnabarinus, B. brassicae, and B. xylophilus. All of the derivatives showed good-to-excellent activity against three insect species tested, with LC50 values ranging from 0.00761 to 0.35496 mmol/L. Remarkably, all of the compounds were more potent than CPT against T. Cinnabarinus, and compounds 4d and 4c displayed superior activity (LC50 0.00761 mmol/L and 0.00942 mmol/L, respectively) compared with CPT (LC50 0.19719 mmol/L) against T. Cinnabarinus. Based on the observed bioactivities, preliminary structure-activity relationship (SAR) correlations were also discussed. Furthermore, a three-dimensional quantitative structure-activity relationship (3D-QSAR) model using comparative molecular field analysis (CoMFA) was built. The model gave statistically significant results with the cross-validated q2 values of 0.580 and correlation coefficient r2 of 0.991 and  of 0.993. The QSAR analysis indicated that the size of the substituents play an important in the activity of 7-modified camptothecin derivatives. These findings will pave the way for further design, structural optimization, and development of camptothecin-derived compounds as pesticidal agents.

  7. A Quantitative Structure-Activity Relationships (QSAR Study of Piperine Based Derivatives with Leishmanicidal Activity

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    Edilson Beserra Alencar Filho

    2017-04-01

    Full Text Available Leishmaniasis is a parasitic disease which represents a serious public health problem in developing countries. It is considered a neglected tropical disease, for which there is little initiative in the search for therapeutic alternatives by pharmaceutical industry. Natural products remain a great source of inspiration for obtaining bioactive molecules. In 2010, Singh and co-workers published the synthesis and in vitro biological activity of piperoyl-aminoacid conjugates, as well as of piperine, against cellular cultures of Leishmania donovani. The piperine is an alkaloid isolated from Piper nigrum that has many activities described in the literature. In this work, we present a Quantitative Structure-Activity Study of piperine derivatives tested by Singh and co-workers, aiming to highlight important molecular features for leishmanicidal activity, obtaining a mathematical model to predict the activity of new analogs. Compounds were submitted to a geometry optimization computational procedure at semiempirical level of quantum theory. Molecular descriptors for the set of compounds were calculated by E-Dragon online plataform, followed by a variable selection procedure using Ordered Predictors Selection algorithm. Validation parameters obtained showed that a good QSAR model, based on multiple linear regression, was obtained (R2 = 0.85; Q2 = 0.69, and the following conclusions regarding the structure-activity relationship were elucidated: Compounds with electronegative atoms on different substituent groups of analogs, absence of unsaturation on lateral chain, presence of ester instead of carboxyl, and large volumes (due the presence of additional aromatic rings trends to increase the activity against promastigote forms of leishmania. DOI: http://dx.doi.org/10.17807/orbital.v9i1.893

  8. QSAR STUDY OF BENZIMIDAZOLE DERIVATIVES INHIBITION ON ...

    African Journals Online (AJOL)

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    The paper describes a quantitative structure-activity relationship (QSAR) study of IC50 values of benzimidazole ... neural network model is a fully-connected, feed forward back propagation network with a 5-4-1 architecture. Standard ... volume, molecular surface area, hydrophobicity (Log P), hydration energy and molecular.

  9. a QSAR Study

    African Journals Online (AJOL)

    DK

    describe a mathematical relationship between the structural features of ... Tetrahymena pyriformis. Models based on different kinds of logP (calculated values for. AlogP, MlogP and ClogP), are compared to the optimal model constructed using a single 3D ... molecular mechanics method (Polack-Ribiere algorithm). The final ...

  10. Semisynthesis and quantitative structure-activity relationship (QSAR) study of some cholesterol-based hydrazone derivatives as insecticidal agents.

    Science.gov (United States)

    Yang, Chun; Shao, Yonghua; Zhi, Xiaoyan; Huan, Qu; Yu, Xiang; Yao, Xiaojun; Xu, Hui

    2013-09-01

    In continuation of our program aimed at the discovery and development of natural-product-based insecticidal agents, four series of novel cholesterol-based hydrazone derivatives were synthesized, and their insecticidal activity was tested against the pre-third-instar larvae of oriental armyworm, Mythimna separata (Walker) in vivo at 1mg/mL. All the derivatives showed the better insecticidal activity than their precursor cholesterol. Quantitative structure-activity relationship (QSAR) model demonstrated that six descriptors such as RDF085v, Mor06u, Mor11u, Dv, HATS0v and H-046, are likely to influence the insecticidal activity of these compounds. Among them, two important ones are the Mor06u and RDF085v. Copyright © 2013 Elsevier Ltd. All rights reserved.

  11. Biomacromolecular quantitative structure-activity relationship (BioQSAR): a proof-of-concept study on the modeling, prediction and interpretation of protein-protein binding affinity.

    Science.gov (United States)

    Zhou, Peng; Wang, Congcong; Tian, Feifei; Ren, Yanrong; Yang, Chao; Huang, Jian

    2013-01-01

    Quantitative structure-activity relationship (QSAR), a regression modeling methodology that establishes statistical correlation between structure feature and apparent behavior for a series of congeneric molecules quantitatively, has been widely used to evaluate the activity, toxicity and property of various small-molecule compounds such as drugs, toxicants and surfactants. However, it is surprising to see that such useful technique has only very limited applications to biomacromolecules, albeit the solved 3D atom-resolution structures of proteins, nucleic acids and their complexes have accumulated rapidly in past decades. Here, we present a proof-of-concept paradigm for the modeling, prediction and interpretation of the binding affinity of 144 sequence-nonredundant, structure-available and affinity-known protein complexes (Kastritis et al. Protein Sci 20:482-491, 2011) using a biomacromolecular QSAR (BioQSAR) scheme. We demonstrate that the modeling performance and predictive power of BioQSAR are comparable to or even better than that of traditional knowledge-based strategies, mechanism-type methods and empirical scoring algorithms, while BioQSAR possesses certain additional features compared to the traditional methods, such as adaptability, interpretability, deep-validation and high-efficiency. The BioQSAR scheme could be readily modified to infer the biological behavior and functions of other biomacromolecules, if their X-ray crystal structures, NMR conformation assemblies or computationally modeled structures are available.

  12. QSAR study of benzimidazole derivatives inhibition on escherichia ...

    African Journals Online (AJOL)

    The paper describes a quantitative structure-activity relationship (QSAR) study of IC50 values of benzimidazole derivatives on escherichia coli methionine aminopeptidase. The activity of the 32 inhibitors has been estimated by means of multiple linear regression (MLR) and artificial neural network (ANN) techniques.

  13. Three-dimensional quantitative structure activity relationship (QSAR) of cytotoxic active 3,5-diaryl-4,5-dihydropyrazole analogs: a comparative molecular field analysis (CoMFA) revisited study.

    Science.gov (United States)

    Hamad Elgazwy, Abdel-Sattar S; Soliman, Daliah S; Atta-Allah, Saad R; Ibrahim, Diaa A

    2012-05-30

    In vitro antitumor evaluation of the synthesized 46 compounds of 3,5-diaryl-4,5-dihydropyrazoles against EAC cell lines and 3D QSAR study using pharmacophore and Comparative Molecular Field Analysis (CoMFA) methods were described. CoMFA derived QSAR model shows a good conventional squared correlation coefficient r2 and cross validated correlation coefficient r2cv 0.896 and 0.568 respectively. In this analysis steric and electrostatic field contribute to the QSAR equation by 70% and 30% respectively, suggesting that variation in biological activity of the compounds is dominated by differences in steric (van der Waals) interactions. To visualize the CoMFA steric and electrostatic field from partial least squares (PLS) analysis, contour maps are plotted as percentage contribution to the QSAR equation and are associated with the differences in biological activity. Pyrazole derivatives exhibit a wide range of biological properties including promising antitumor activity. Furthermore, Aldol condensation assisted organic synthesis has delivered rapid routes to N-containing heterocycles, including pyrazoles. Combining these features, the use of chalconisation-assisted processes will provide rapid access to a targeted dihydropyrazoles library bearing a hydrazino 3D QSAR study using pharmacophore and Comparative Molecular Field Analysis (CoMFA) methods were described for evaluation of antioxidant properties. Chalcones promoted 1 of the 2 steps in a rapid, convergent synthesis of a small library of hydrazinyl pyrazole derivatives, all of which exhibited significant antitumor activity against Ehrlich Ascites Carcinoma (EAC) human tumor cell line comparable to that of the natural anticancer doxorubicin, as a reference standard during this study. In order to understand the observed pharmacological properties, quantitative structure-activity relationship (3D QSAR) study was initiated. Chalcones heating provides a rapid and expedient route to a series of pyrazoles to investigate

  14. Three-dimensional quantitative structure activity relationship (QSAR of cytotoxic active 3,5-diaryl-4,5-dihydropyrazole analogs: a comparative molecular field analysis (CoMFA revisited study

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    Hamad Elgazwy Abdel-Sattar S

    2012-05-01

    Full Text Available Abstract In vitro antitumor evaluation of the synthesized 46 compounds of 3,5-diaryl-4,5-dihydropyrazoles against EAC cell lines and 3D QSAR study using pharmacophore and Comparative Molecular Field Analysis (CoMFA methods were described. CoMFA derived QSAR model shows a good conventional squared correlation coefficient r2 and cross validated correlation coefficient r2cv 0.896 and 0.568 respectively. In this analysis steric and electrostatic field contribute to the QSAR equation by 70% and 30% respectively, suggesting that variation in biological activity of the compounds is dominated by differences in steric (van der Waals interactions. To visualize the CoMFA steric and electrostatic field from partial least squares (PLS analysis, contour maps are plotted as percentage contribution to the QSAR equation and are associated with the differences in biological activity. Background Pyrazole derivatives exhibit a wide range of biological properties including promising antitumor activity. Furthermore, Aldol condensation assisted organic synthesis has delivered rapid routes to N-containing heterocycles, including pyrazoles. Combining these features, the use of chalconisation-assisted processes will provide rapid access to a targeted dihydropyrazoles library bearing a hydrazino 3D QSAR study using pharmacophore and Comparative Molecular Field Analysis (CoMFA methods were described for evaluation of antioxidant properties. Results Chalcones promoted 1 of the 2 steps in a rapid, convergent synthesis of a small library of hydrazinyl pyrazole derivatives, all of which exhibited significant antitumor activity against Ehrlich Ascites Carcinoma (EAC human tumor cell line comparable to that of the natural anticancer doxorubicin, as a reference standard during this study. In order to understand the observed pharmacological properties, quantitative structure-activity relationship (3D QSAR study was initiated. Conclusions Chalcones heating provides a rapid and

  15. Toxicity challenges in environmental chemicals: Prediction of human plasma protein binding through quantitative structure-activity relationship (QSAR) models

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    The present study explores the merit of utilizing available pharmaceutical data to construct a quantitative structure-activity relationship (QSAR) for prediction of the fraction of a chemical unbound to plasma protein (Fub) in environmentally relevant compounds. Independent model...

  16. Developing sensor activity relationships for the JPL electronic nose sensors using molecular modeling and QSAR techniques

    Science.gov (United States)

    Shevade, A. V.; Ryan, M. A.; Homer, M. L.; Jewell, A. D.; Zhou, H.; Manatt, K.; Kisor, A. K.

    2005-01-01

    We report a Quantitative Structure-Activity Relationships (QSAR) study using Genetic Function Approximations (GFA) to describe the polymer-carbon composite sensor activities in the JPL Electronic Nose, when exposed to chemical vapors at parts-per-million concentration levels.

  17. Prediction of the relationship between the structural features of andrographolide derivatives and α-glucosidase inhibitory activity: a quantitative structure-activity relationship (QSAR) study.

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    Moorthy, N S Hari Narayana; Ramos, Maria J; Fernandes, Pedro A

    2011-02-01

    In order to predict the structural features responsible for α-glucosidase inhibitory activity, a quantitative structure-activity relationship (QSAR) analysis was performed on a series of andrographolide derivatives. To determine the quantitative relationship for the statistically significant models in terms of r (>0.8), F (99%) and Q(2) (>0.71) values were selected. The promising results we obtained could be used to predict the structural requirements for the inhibition of α-glucosidase activity. The models developed included: subdivided surface area, adjacency, surface volume and shape, molecular orbital package (MOPAC) and partial charge descriptors and showed a high correlation with the inhibitory activity. The descriptors used revealed that a van der Waals (vdW) surface with significant polar volume is favourable to the activity. The positive effect of the shape descriptors; PM3-LUMO and vsurf_wp7 and the negative effect of GCUT_PEOE_2 indicated that the active site may contain some nucleophilic positions that could interact with the ligand and the hydrogen acceptor and/or donor groups for hydrogen bonding with inhibitors.

  18. Synthesis, Cytotoxic Activity on Leukemia Cell Lines and Quantitative Structure-Activity Relationships (QSAR) Studies of Morita-Baylis-Hillman Adducts.

    Science.gov (United States)

    Lima-, Claudio G; Faheina-Martins, Gláucia V; Bomfim, Caio C B; Dantas, Bruna B; Silva, Everton P; Araújo, Demetrius A M de; Filho, Edilson B A; Vasconcellos, Mário L A A

    2016-01-01

    The Morita-Baylis-Hillman reaction is an organocatalyzed chemical transformation that allows access to small poly-functionalized molecules and has considerable synthetic potential and promising biological profiles. The Morita-Baylis-Hillman adducts (MBHA) are a new class of bioactive compounds and highlight its potentialities to the discovery of new cheaper and efficient drugs, e.g. as anti-Leishmania chagasi and Leishmania amazonensis, anti- Trypanosoma cruzi, anti-Plasmodium falciparum and Plasmodium berghei, lethal against Biomphalaria glabrata, antibacterial, antifungal, herbicide and others. The goal of this work is to describe the primary cytotoxic activities against strains of human leukemia HL-60 cell line for thirty-four Morita-Baylis- Hillman adducts (MBHA), followed by a Quantitative Structure-Activity Relationships study (QSAR). The conventional or microwave-assisted syntheses of MBHA, derived from substituted aromatics or Isatin, were performed in good to excellent yields (70-100%) in short reaction times, using protocols recently developed by us. Isatin derivatives, MBHA 31 and 32, were the most active in this congener series of compounds, with IC50 values of 10.8 μM and 7.8 μM, respectively. The primary cytotoxic activities against chronic leukemia cells (K562) were also evaluated to these two most active compounds (MBHA 31 and 32), presenting IC50 values of 53 μM and 43 μM respectively. QSAR study was performed considering 3D, 2D and constitutional molecular descriptors. These were selected from Ordered Predictor Selection algorithm and submitted to Partial Least Squares Modeling. We present an interesting investigation about cytotoxic activities on human leukemia cell line (HL-60) for 34 synthetic MBHA. In a good way we discovered that the most cytotoxic compounds (31-32, 10.8 μM and 7.8 μM respectively) were also prepared quantitatively (100% yields) in a short reaction time using microwave irradiation. We demonstrate that 31 and 32 induced

  19. Docking and QSAR Studies of Camptothecin Derivatives as Inhibitor of DNA Topoisomerase-I

    OpenAIRE

    Dharmendra K. Yadav; Feroz Khan; Srivastava, Santosh K.

    2011-01-01

    Camptothecin (CPT) is a cytotoxic quinoline alkaloid which inhibits the DNA enzyme Topoisomerase-I (Topo-I) and has shown remarkable anticancer activity in preliminary clinical trials. The major limitation is its low solubility and high adverse reaction. In the studied work, we performed molecular docking of CPT derivatives against Topo-I and developed the quantitative structure activity relationship (QSAR) model for anticancer activity screening. For QSAR, we used CPT and other anticancer dr...

  20. Synthesis and quantitative structure-activity relationship (QSAR) study of novel 4-acyloxypodophyllotoxin derivatives modified in the A and C rings as insecticidal agents.

    Science.gov (United States)

    He, Shuzhen; Shao, Yonghua; Fan, Lingling; Che, Zhiping; Xu, Hui; Zhi, Xiaoyan; Wang, Juanjuan; Yao, Xiaojun; Qu, Huan

    2013-01-23

    In continuation of our program aimed at the discovery and development of natural-product-based insecticidal agents, we have synthesized three series of novel 4-acyloxy compounds derived from podophyllotoxin modified in the A and C rings, which is isolated as the main secondary metabolite from the roots and rhizomes of Podophyllum hexandrum . Their insecticidal activity was preliminarily evaluated against the pre-third-instar larvae of Mythimna separata in vivo. Compound 9g displayed the best promising insecticidal activity. It revealed that cleavage of the 6,7-methylenedioxy group of podophyllotoxin will lead to a less active compound and that the C-4 position of podophyllotoxin was the important modification location. A quantitative structure-activity relationship (QSAR) model was developed by genetic algorithm combined with multiple linear regression (GA-MLR). For this model, the squared correlation coefficient (R(2)) is 0.914, the leave-one-out cross-validation correlation coefficient (Q(2)(LOO)) is 0.881, and the root-mean-square error (RMSE) is 0.024. Five descriptors, BEHm2, Mor14v, Wap, G1v, and RDF020e, are likely to influence the biological activity of these compounds. Among them, two important ones are BEHm2 and Mor14v. This study will pave the way for further design, structural modification, and development of podophyllotoxin derivatives as insecticidal agents.

  1. Receptor-based 3D-QSAR in Drug Design: Methods and Applications in Kinase Studies.

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    Fang, Cheng; Xiao, Zhiyan

    2016-01-01

    Receptor-based 3D-QSAR strategy represents a superior integration of structure-based drug design (SBDD) and three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis. It combines the accurate prediction of ligand poses by the SBDD approach with the good predictability and interpretability of statistical models derived from the 3D-QSAR approach. Extensive efforts have been devoted to the development of receptor-based 3D-QSAR methods and two alternative approaches have been exploited. One associates with computing the binding interactions between a receptor and a ligand to generate structure-based descriptors for QSAR analyses. The other concerns the application of various docking protocols to generate optimal ligand poses so as to provide reliable molecular alignments for the conventional 3D-QSAR operations. This review highlights new concepts and methodologies recently developed in the field of receptorbased 3D-QSAR, and in particular, covers its application in kinase studies.

  2. Multivariate QSAR

    Directory of Open Access Journals (Sweden)

    Ferreira Márcia M. C.

    2002-01-01

    Full Text Available In this work, the chemometric techniques most frequently used in QSAR (quantitative structure-activity relationships studies are reviewed. They are introduced in chronological order, beginning with Hansch analysis and the exploratory data analysis methods of principal components and hierarchical clustering (PCA and HCA. Principal component regression and partial least squares regression methods (PCR and PLS are discussed, followed by the pattern recognition methods (KNN and SIMCA. Different applications are presented to illustrate these chemometric techniques. The methodology used for regression in 3D-QSAR is presented (unfolding PLS. Finally, the higher order method called Multilinear PLS, already used in analytical chemistry but not yet explored by the QSAR community, is introduced. This method maintains the multiway structure of the data and has several advantages over bilinear PLS including speed in calculation, simplicity and stability, since the number of parameters to be estimated can be greatly reduced.

  3. Synthesis and quantitative structure-activity relationship (QSAR) study of novel N-arylsulfonyl-3-acylindole arylcarbonyl hydrazone derivatives as nematicidal agents.

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    Che, Zhiping; Zhang, Shaoyong; Shao, Yonghua; Fan, Lingling; Xu, Hui; Yu, Xiang; Zhi, Xiaoyan; Yao, Xiaojun; Zhang, Rui

    2013-06-19

    In continuation of our program aimed at the discovery and development of natural-product-based pesticidal agents, 54 novel N-arylsulfonyl-3-acylindole arylcarbonyl hydrazone derivatives were prepared, and their structures were well characterized by ¹H NMR, ¹³C NMR, HRMS, ESI-MS, and mp. Their nematicidal activity was evaluated against that of the pine wood nematode, Bursaphelenchus xylophilus in vivo. Among all of the derivatives, especially V-12 and V-39 displayed the best promising nematicidal activity with LC₅₀ values of 1.0969 and 1.2632 mg/L, respectively. This suggested that introduction of R¹ and R² together as the electron-withdrawing substituents, R³ as the methyl group, and R⁴ as the phenyl with the electron-donating substituents could be taken into account for further preparation of these kinds of compounds as nematicidal agents. Six selected descriptors are a WHIM descriptor (E1m), two GETAWAY descriptors (R1m+ and R3m+), a Burden eigenvalues descriptor (BEHm8), and two edge-adjacency index descriptors (EEig05x and EEig13d). Quantitative structure-activity relationship (QSAR) studies demonstrated that the structural factors, such as molecular mass (a negative correlation with the bioactivity) and molecular polarity (a positive correlation with bioactivity), are likely to govern the nematicidal activities of these compounds. For this model, the correlation coefficient (R²(training set)), the leave-one-out cross-validation correlation coefficient (Q²(LOO)), and the 7-fold cross-validation correlation coefficient (Q²(7-fold)) were 0.791, 0.701, and 0.715, respectively. The external cross-validation correlation coefficient (Q²ext) and the root-mean-square error for the test set (RMSE(test set)) were 0.774 and 3.412, respectively. This study will pave the way for future design, structural modification, and development of indole derivatives as nematicidal agents.

  4. Drug interaction study of natural steroids from herbs specifically toward human UDP-glucuronosyltransferase (UGT) 1A4 and their quantitative structure activity relationship (QSAR) analysis for prediction.

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    Xu, Min; Dong, Peipei; Tian, Xiangge; Wang, Chao; Huo, Xiaokui; Zhang, Baojing; Wu, Lijun; Deng, Sa; Ma, Xiaochi

    2016-08-01

    The wide application of herbal medicines and foods containing steroids has resulted in the high risk of herb-drug interactions (HDIs). The present study aims to evaluate the inhibition potential of 43 natural steroids from herb medicines toward human UDP- glucuronosyltransferases (UGTs). A remarkable structure-dependent inhibition toward UGT1A4 was observed in vitro. Some natural steroids such as gitogenin, tigogenin, and solasodine were found to be the novel selective inhibitors of UGT1A4, and did not inhibit the activities of major human CYP isoforms. To clarify the possibility of the in vivo interaction of common steroids and clinical drugs, the kinetic inhibition type and related kinetic parameters (Ki) were measured. The target compounds 2-6 and 15, competitively inhibited the UGT1A4-catalyzed trifluoperazine glucuronidation reaction, with Ki values of 0.6, 0.18, 1.1, 0.7, 0.8, and 12.3μM, respectively. And this inhibition of steroids towards UGT1A4 was also verified in human primary hepatocytes. Furthermore, a quantitative structure-activity relationship (QSAR) of steroids with inhibitory effects toward human UGT1A4 isoform was established using the computational methods. Our findings elucidate the potential for in vivo HDI effects of steroids in herbal medicine and foods, with the clinical dr ugs eliminated by UGT1A4, and reveal the vital pharamcophoric requirement of natural steroids for UGT1A4 inhibition activity. Copyright © 2016 Elsevier Ltd. All rights reserved.

  5. Synthesis and quantitative structure-activity relationship (QSAR) study of novel isoxazoline and oxime derivatives of podophyllotoxin as insecticidal agents.

    Science.gov (United States)

    Wang, Yi; Shao, Yonghua; Wang, Yangyang; Fan, Lingling; Yu, Xiang; Zhi, Xiaoyan; Yang, Chun; Qu, Huan; Yao, Xiaojun; Xu, Hui

    2012-08-29

    In continuation of our program aimed at the discovery and development of natural-product-based insecticidal agents, 33 isoxazoline and oxime derivatives of podophyllotoxin modified in the C and D rings were synthesized and their structures were characterized by Proton nuclear magnetic resonance ((1)H NMR), high-resolution mass spectrometry (HRMS), electrospray ionization-mass spectrometry (ESI-MS), optical rotation, melting point (mp), and infrared (IR) spectroscopy. The stereochemical configurations of compounds 5e, 5f, and 9f were unambiguously determined by X-ray crystallography. Their insecticidal activity was evaluated against the pre-third-instar larvae of northern armyworm, Mythimna separata (Walker), in vivo. Compounds 5e, 9c, 11g, and 11h especially exhibited more promising insecticidal activity than toosendanin, a commercial botanical insecticide extracted from Melia azedarach . A genetic algorithm combined with multiple linear regression (GA-MLR) calculation is performed by the MOBY DIGS package. Five selected descriptors are as follows: one two-dimensional (2D) autocorrelation descriptor (GATS4e), one edge adjacency indice (EEig06x), one RDF descriptor (RDF080v), one three-dimensional (3D) MoRSE descriptor (Mor09v), and one atom-centered fragment (H-052) descriptor. Quantitative structure-activity relationship studies demonstrated that the insecticidal activity of these compounds was mainly influenced by many factors, such as electronic distribution, steric factors, etc. For this model, the standard deviation error in prediction (SDEP) is 0.0592, the correlation coefficient (R(2)) is 0.861, and the leave-one-out cross-validation correlation coefficient (Q(2)loo) is 0.797.

  6. Quantitative Structure--Activity Relationship (QSAR) for the Oxidation of Trace Organic Contaminants by Sulfate Radical.

    Science.gov (United States)

    Xiao, Ruiyang; Ye, Tiantian; Wei, Zongsu; Luo, Shuang; Yang, Zhihui; Spinney, Richard

    2015-11-17

    The sulfate radical anion (SO4•–) based oxidation of trace organic contaminants (TrOCs) has recently received great attention due to its high reactivity and low selectivity. In this study, a meta-analysis was conducted to better understand the role of functional groups on the reactivity between SO4•– and TrOCs. The results indicate that compounds in which electron transfer and addition channels dominate tend to exhibit a faster second-order rate constants (kSO4•–) than that of H–atom abstraction, corroborating the SO4•– reactivity and mechanisms observed in the individual studies. Then, a quantitative structure activity relationship (QSAR) model was developed using a sequential approach with constitutional, geometrical, electrostatic, and quantum chemical descriptors. Two descriptors, ELUMO and EHOMO energy gap (ELUMO–EHOMO) and the ratio of oxygen atoms to carbon atoms (#O:C), were found to mechanistically and statistically affect kSO4•– to a great extent with the standardized QSAR model: ln kSO4•– = 26.8–3.97 × #O:C – 0.746 × (ELUMO–EHOMO). In addition, the correlation analysis indicates that there is no dominant reaction channel for SO4•– reactions with various structurally diverse compounds. Our QSAR model provides a robust predictive tool for estimating emerging micropollutants removal using SO4•– during wastewater treatment processes.

  7. Three-dimensional quantitative structure activity relationship (QSAR) of cytotoxic active 3,5-diaryl-4,5-dihydropyrazole analogs: a comparative molecular field analysis (CoMFA) revisited study

    OpenAIRE

    Hamad Elgazwy Abdel-Sattar S; Soliman DaliaH S; Atta-Allah Saad R; Ibrahim Diaa A

    2012-01-01

    Abstract In vitro antitumor evaluation of the synthesized 46 compounds of 3,5-diaryl-4,5-dihydropyrazoles against EAC cell lines and 3D QSAR study using pharmacophore and Comparative Molecular Field Analysis (CoMFA) methods were described. CoMFA derived QSAR model shows a good conventional squared correlation coefficient r2 and cross validated correlation coefficient r2cv 0.896 and 0.568 respectively. In this analysis steric and electrostatic field contribute to the QSAR equation by 70% and 3...

  8. Validation of Quantitative Structure-Activity Relationship (QSAR Model for Photosensitizer Activity Prediction

    Directory of Open Access Journals (Sweden)

    Sharifuddin M. Zain

    2011-11-01

    Full Text Available Photodynamic therapy is a relatively new treatment method for cancer which utilizes a combination of oxygen, a photosensitizer and light to generate reactive singlet oxygen that eradicates tumors via direct cell-killing, vasculature damage and engagement of the immune system. Most of photosensitizers that are in clinical and pre-clinical assessments, or those that are already approved for clinical use, are mainly based on cyclic tetrapyrroles. In an attempt to discover new effective photosensitizers, we report the use of the quantitative structure-activity relationship (QSAR method to develop a model that could correlate the structural features of cyclic tetrapyrrole-based compounds with their photodynamic therapy (PDT activity. In this study, a set of 36 porphyrin derivatives was used in the model development where 24 of these compounds were in the training set and the remaining 12 compounds were in the test set. The development of the QSAR model involved the use of the multiple linear regression analysis (MLRA method. Based on the method, r2 value, r2 (CV value and r2 prediction value of 0.87, 0.71 and 0.70 were obtained. The QSAR model was also employed to predict the experimental compounds in an external test set. This external test set comprises 20 porphyrin-based compounds with experimental IC50 values ranging from 0.39 µM to 7.04 µM. Thus the model showed good correlative and predictive ability, with a predictive correlation coefficient (r2 prediction for external test set of 0.52. The developed QSAR model was used to discover some compounds as new lead photosensitizers from this external test set.

  9. Hologram QSAR Studies of Antiprotozoal Activities of Sesquiterpene Lactones

    Directory of Open Access Journals (Sweden)

    Gustavo H. G. Trossini

    2014-07-01

    Full Text Available Infectious diseases such as trypanosomiasis and leishmaniasis are considered neglected tropical diseases due the lack for many years of research and development into new drug treatments besides the high incidence of mortality and the lack of current safe and effective drug therapies. Natural products such as sesquiterpene lactones have shown activity against T. brucei and L. donovani, the parasites responsible for these neglected diseases. To evaluate structure activity relationships, HQSAR models were constructed to relate a series of 40 sesquiterpene lactones (STLs with activity against T. brucei, T. cruzi, L. donovani and P. falciparum and also with their cytotoxicity. All constructed models showed good internal (leave-one-out q2 values ranging from 0.637 to 0.775 and external validation coefficients (r2test values ranging from 0.653 to 0.944. From HQSAR contribution maps, several differences between the most and least potent compounds were found. The fragment contribution of PLS-generated models confirmed the results of previous QSAR studies that the presence of α,β-unsatured carbonyl groups is fundamental to biological activity. QSAR models for the activity of these compounds against T. cruzi, L. donovani and P. falciparum are reported here for the first time. The constructed HQSAR models are suitable to predict the activity of untested STLs.

  10. Hologram QSAR studies of antiprotozoal activities of sesquiterpene lactones.

    Science.gov (United States)

    Trossini, Gustavo H G; Maltarollo, Vinícius G; Schmidt, Thomas J

    2014-07-18

    Infectious diseases such as trypanosomiasis and leishmaniasis are considered neglected tropical diseases due the lack for many years of research and development into new drug treatments besides the high incidence of mortality and the lack of current safe and effective drug therapies. Natural products such as sesquiterpene lactones have shown activity against T. brucei and L. donovani, the parasites responsible for these neglected diseases. To evaluate structure activity relationships, HQSAR models were constructed to relate a series of 40 sesquiterpene lactones (STLs) with activity against T. brucei, T. cruzi, L. donovani and P. falciparum and also with their cytotoxicity. All constructed models showed good internal (leave-one-out q2 values ranging from 0.637 to 0.775) and external validation coefficients (r2test values ranging from 0.653 to 0.944). From HQSAR contribution maps, several differences between the most and least potent compounds were found. The fragment contribution of PLS-generated models confirmed the results of previous QSAR studies that the presence of α,β-unsatured carbonyl groups is fundamental to biological activity. QSAR models for the activity of these compounds against T. cruzi, L. donovani and P. falciparum are reported here for the first time. The constructed HQSAR models are suitable to predict the activity of untested STLs.

  11. Three-Dimensional Quantitative Structural Activity Relationship (3D-QSAR Studies of Some 1,5-Diarylpyrazoles: Analogue Based Design of Selective Cyclooxygenase-2 Inhibitors

    Directory of Open Access Journals (Sweden)

    Hosahalli S. Subramanya

    2000-07-01

    Full Text Available Selective cyclooxygenase inhibitors have attracted much attention in recent times in the design of new non-steroidal anti-inflammatory drugs (NSAID. 3D-QSAR studies have been performed on a series of 1,5-diarylpyrazoles that act as selective cyclooxygenase-2 (COX-2 inhibitors, using three different methods: comparative molecular field analysis (CoMFA with partial least squares (PLS fit; molecular field analysis (MFA and; receptor surface analysis (RSA with genetic function algorithms (GFA. The analyses were carried out on 30 analogues of which 25 were used in the training set and the rest considered for the test set. These studies produced reasonably good predictive models with high cross-validated and conventional r2 values in all the three cases.

  12. Three-dimensional quantitative structure activity relationship (QSAR) of cytotoxic active 3,5-diaryl-4,5-dihydropyrazole analogs: a comparative molecular field analysis (CoMFA) revisited study

    National Research Council Canada - National Science Library

    Hamad Elgazwy, Abdel-Sattar S; Soliman, DaliaH S; Atta-Allah, Saad R; Ibrahim, Diaa A

    2012-01-01

    In vitro antitumor evaluation of the synthesized 46 compounds of 3,5-diaryl-4,5-dihydropyrazoles against EAC cell lines and 3D QSAR study using pharmacophore and Comparative Molecular Field Analysis (CoMFA...

  13. Imidazolium Ionic Liquids as Potential Anti-Candida Inhibitors: QSAR Modeling and Experimental Studies.

    Science.gov (United States)

    Hodyna, Diana; Kovalishyn, Vasyl; Rogalsky, Sergiy; Blagodatnyi, Volodymyr; Metelytsia, Larisa

    2016-01-01

    Quantitative structure-activity relationships (QSAR) of imidazolium ionic liquids (ILs) as inhibitors of C. albicans collection strains (IOA-109, KCTC 1940, ATCC 10231) have been studied. Predictive QSAR models were built using different descriptor sets for a set of 88 ionic liquids with known minimum inhibitory concentrations (MIC) against C. albicans. We applied the state-of-the-art QSAR methodologies such as WEKA Random Forest (RF) as a binary classifier, Associative Neural Networks (ASNN) and k-Nearest Neighbors (k-NN) to build continuum non-linear regression models. The obtained models were validated using a 5-fold cross-validation approach and resulted in the prediction accuracies of 80% ± 5.0 for the classification models and q2 = 0.73-0.87 for the non-linear regression models. Biological testing of newly synthesized 1,3-dialkylimidazolium ionic liquids with predicted activity was performed by disco-diffusion method against C. albicans ATCC 10231 M885 strain and clinical isolates C. albicans, C. krusei and C. glabrata strains. The high percentage of coincidence between the QSAR predictions and the experimental results confirmed the high predictive power of the developed QSAR models within the applicability domain of new imidazolium ionic liquids.

  14. In silico study of in vitro GPCR assays by QSAR modeling ...

    Science.gov (United States)

    The U.S. EPA is screening thousands of chemicals of environmental interest in hundreds of in vitro high-throughput screening (HTS) assays (the ToxCast program). One goal is to prioritize chemicals for more detailed analyses based on activity in molecular initiating events (MIE) of adverse outcome pathways (AOPs). However, the chemical space of interest for environmental exposure is much wider than this set of chemicals. Thus, there is a need to fill data gaps with in silico methods, and quantitative structure-activity relationships (QSARs) are a proven and cost effective approach to predict biological activity. ToxCast in turn provides relatively large datasets that are ideal for training and testing QSAR models. The overall goal of the study described here was to develop QSAR models to fill the data gaps in a larger environmental database of ~32k structures. The specific aim of the current work was to build QSAR models for 18 G-Protein Coupled Receptor (GPCR) assays, part of the aminergic category. Two QSAR modeling strategies were adopted: classification models were developed to separate chemicals into active/non-active classes, and then regression models were built to predict the potency values of the bioassays for the active chemicals. Multiple software programs were used to calculate constitutional, topological and substructural molecular descriptors from two-dimensional (2D) chemical structures. Model-fitting methods included PLSDA (partial least squares d

  15. QSAR study of prolylcarboxypeptidase inhibitors by genetic ...

    Indian Academy of Sciences (India)

    The hierarchical clustering method was used to classify the dataset into training and test subsets. The important descriptors were selected with the aid of the genetic algorithm method. The QSAR model was constructed, using the multiple linear regressions (MLR), and its robustness and predictability were verified by internal ...

  16. The Three Dimensional Quantitative Structure Activity Relationships (3D-QSAR and Docking Studies of Curcumin Derivatives as Androgen Receptor Antagonists

    Directory of Open Access Journals (Sweden)

    Jing Yang

    2012-05-01

    Full Text Available Androgen receptor antagonists have been proved to be effective anti-prostate cancer agents. 3D-QSAR and Molecular docking methods were performed on curcumin derivatives as androgen receptor antagonists. The bioactive conformation was explored by docking the potent compound 29 into the binding site of AR. The constructed Comparative Molecular Field Analysis (CoMFA and Comparative Similarity Indices Analysis (CoMSIA models produced statistically significant results with the cross-validated correlation coefficients q2 of 0.658 and 0.567, non-cross-validated correlation coefficients r2 of 0.988 and 0.978, and predicted correction coefficients r2pred of 0.715 and 0.793, respectively. These results ensure the CoMFA and CoMSIA models as a tool to guide the design of novel potent AR antagonists. A set of 30 new analogs were proposed by utilizing the results revealed in the present study, and were predicted with potential activities in the developed models.

  17. Development of acute toxicity quantitative structure activity relationships (QSAR) and their use in linear alkylbenzene sulfonate species sensitivity distributions.

    Science.gov (United States)

    Belanger, Scott E; Brill, Jessica L; Rawlings, Jane M; Price, Brad B

    2016-07-01

    Linear Alkylbenzene Sulfonate (LAS) is high tonnage and widely dispersed anionic surfactant used by the consumer products sector. A range of homologous structures are used in laundry applications that differ primarily on the length of the hydrophobic alkyl chain. This research summarizes the development of a set of acute toxicity QSARs (Quantitative Structure Activity Relationships) for fathead minnows (Pimephales promelas) and daphnids (Daphnia magna, Ceriodaphnia dubia) using accepted test guideline approaches. A series of studies on pure chain length LAS from C10 to C14 were used to develop the QSARs and the robustness of the QSARs was tested by evaluation of two technical mixtures of differing compositions. All QSARs were high quality (R(2) were 0.965-0.997, p Sensitivity Distributions (SSDs) for various chain lengths of interest. Mixtures include environmental distributions measured from exposure monitoring surveys of wastewater effluents, various commercial mixtures, or specific chain lengths. SSD 5th percentile hazardous concentrations (HC5s) ranged from 0.129 to 0.254 mg/L for wastewater effluents containing an average of 11.26-12 alkyl carbons. The SSDs are considered highly robust given the breadth of species (n = 19), use of most sensitive endpoints from true chronic studies and the quality of the underlying statistical properties of the SSD itself. The data continue to indicate a low hazard to the environment relative to expected environmental concentrations. Copyright © 2016 Elsevier Ltd. All rights reserved.

  18. Structure-based and multiple potential three-dimensional quantitative structure-activity relationship (SB-MP-3D-QSAR) for inhibitor design.

    Science.gov (United States)

    Du, Qi-Shi; Gao, Jing; Wei, Yu-Tuo; Du, Li-Qin; Wang, Shu-Qing; Huang, Ri-Bo

    2012-04-23

    The inhibitions of enzymes (proteins) are determined by the binding interactions between ligands and targeting proteins. However, traditional QSAR (quantitative structure-activity relationship) is a one-side technique, only considering the structures and physicochemical properties of inhibitors. In this study, the structure-based and multiple potential three-dimensional quantitative structure-activity relationship (SB-MP-3D-QSAR) is presented, in which the structural information of host protein is involved in the QSAR calculations. The SB-MP-3D-QSAR actually is a combinational method of docking approach and QSAR technique. Multiple docking calculations are performed first between the host protein and ligand molecules in a training set. In the targeting protein, the functional residues are selected, which make the major contribution to the binding free energy. The binding free energy between ligand and targeting protein is the summation of multiple potential energies, including van der Waals energy, electrostatic energy, hydrophobic energy, and hydrogen-bond energy, and may include nonthermodynamic factors. In the foundational QSAR equation, two sets of weighting coefficients {aj} and {bp} are assigned to the potential energy terms and to the functional residues, respectively. The two coefficient sets are solved by using iterative double least-squares (IDLS) technique in the training set. Then, the two sets of weighting coefficients are used to predict the bioactivities of inquired ligands. In an application example, the new developed method obtained much better results than that of docking calculations.

  19. Improvement of multivariate image analysis applied to quantitative structure-activity relationship (QSAR) analysis by using wavelet-principal component analysis ranking variable selection and least-squares support vector machine regression: QSAR study of checkpoint kinase WEE1 inhibitors.

    Science.gov (United States)

    Cormanich, Rodrigo A; Goodarzi, Mohammad; Freitas, Matheus P

    2009-02-01

    Inhibition of tyrosine kinase enzyme WEE1 is an important step for the treatment of cancer. The bioactivities of a series of WEE1 inhibitors have been previously modeled through comparative molecular field analyses (CoMFA and CoMSIA), but a two-dimensional image-based quantitative structure-activity relationship approach has shown to be highly predictive for other compound classes. This method, called multivariate image analysis applied to quantitative structure-activity relationship, was applied here to derive quantitative structure-activity relationship models. Whilst the well-known bilinear and multilinear partial least squares regressions (PLS and N-PLS, respectively) correlated multivariate image analysis descriptors with the corresponding dependent variables only reasonably well, the use of wavelet and principal component ranking as variable selection methods, together with least-squares support vector machine, improved significantly the prediction statistics. These recently implemented mathematical tools, particularly novel in quantitative structure-activity relationship studies, represent an important advance for the development of more predictive quantitative structure-activity relationship models and, consequently, new drugs.

  20. QSAR DataBank repository: open and linked qualitative and quantitative structure-activity relationship models.

    Science.gov (United States)

    Ruusmann, V; Sild, S; Maran, U

    2015-01-01

    Structure-activity relationship models have been used to gain insight into chemical and physical processes in biomedicine, toxicology, biotechnology, etc. for almost a century. They have been recognized as valuable tools in decision support workflows for qualitative and quantitative predictions. The main obstacle preventing broader adoption of quantitative structure-activity relationships [(Q)SARs] is that published models are still relatively difficult to discover, retrieve and redeploy in a modern computer-oriented environment. This publication describes a digital repository that makes in silico (Q)SAR-type descriptive and predictive models archivable, citable and usable in a novel way for most common research and applied science purposes. The QSAR DataBank (QsarDB) repository aims to make the processes and outcomes of in silico modelling work transparent, reproducible and accessible. Briefly, the models are represented in the QsarDB data format and stored in a content-aware repository (a.k.a. smart repository). Content awareness has two dimensions. First, models are organized into collections and then into collection hierarchies based on their metadata. Second, the repository is not only an environment for browsing and downloading models (the QDB archive) but also offers integrated services, such as model analysis and visualization and prediction making. The QsarDB repository unlocks the potential of descriptive and predictive in silico (Q)SAR-type models by allowing new and different types of collaboration between model developers and model users. The key enabling factor is the representation of (Q)SAR models in the QsarDB data format, which makes it easy to preserve and share all relevant data, information and knowledge. Model developers can become more productive by effectively reusing prior art. Model users can make more confident decisions by relying on supporting information that is larger and more diverse than before. Furthermore, the smart repository

  1. AutoWeka: toward an automated data mining software for QSAR and QSPR studies.

    Science.gov (United States)

    Nantasenamat, Chanin; Worachartcheewan, Apilak; Jamsak, Saksiri; Preeyanon, Likit; Shoombuatong, Watshara; Simeon, Saw; Mandi, Prasit; Isarankura-Na-Ayudhya, Chartchalerm; Prachayasittikul, Virapong

    2015-01-01

    In biology and chemistry, a key goal is to discover novel compounds affording potent biological activity or chemical properties. This could be achieved through a chemical intuition-driven trial-and-error process or via data-driven predictive modeling. The latter is based on the concept of quantitative structure-activity/property relationship (QSAR/QSPR) when applied in modeling the biological activity and chemical properties, respectively, of compounds. Data mining is a powerful technology underlying QSAR/QSPR as it harnesses knowledge from large volumes of high-dimensional data via multivariate analysis. Although extremely useful, the technicalities of data mining may overwhelm potential users, especially those in the life sciences. Herein, we aim to lower the barriers to access and utilization of data mining software for QSAR/QSPR studies. AutoWeka is an automated data mining software tool that is powered by the widely used machine learning package Weka. The software provides a user-friendly graphical interface along with an automated parameter search capability. It employs two robust and popular machine learning methods: artificial neural networks and support vector machines. This chapter describes the practical usage of AutoWeka and relevant tools in the development of predictive QSAR/QSPR models. The software is freely available at http://www.mt.mahidol.ac.th/autoweka.

  2. 2D-QSAR study of fullerene nanostructure derivatives as potent HIV-1 protease inhibitors

    Science.gov (United States)

    Barzegar, Abolfazl; Jafari Mousavi, Somaye; Hamidi, Hossein; Sadeghi, Mehdi

    2017-09-01

    The protease of human immunodeficiency virus1 (HIV-PR) is an essential enzyme for antiviral treatments. Carbon nanostructures of fullerene derivatives, have nanoscale dimension with a diameter comparable to the diameter of the active site of HIV-PR which would in turn inhibit HIV. In this research, two dimensional quantitative structure-activity relationships (2D-QSAR) of fullerene derivatives against HIV-PR activity were employed as a powerful tool for elucidation the relationships between structure and experimental observations. QSAR study of 49 fullerene derivatives was performed by employing stepwise-MLR, GAPLS-MLR, and PCA-MLR models for variable (descriptor) selection and model construction. QSAR models were obtained with higher ability to predict the activity of the fullerene derivatives against HIV-PR by a correlation coefficient (R2training) of 0.942, 0.89, and 0.87 as well as R2test values of 0.791, 0.67and 0.674 for stepwise-MLR, GAPLS-MLR, and PCA -MLR models, respectively. Leave-one-out cross-validated correlation coefficient (R2CV) and Y-randomization methods confirmed the models robustness. The descriptors indicated that the HIV-PR inhibition depends on the van der Waals volumes, polarizability, bond order between two atoms and electronegativities of fullerenes derivatives. 2D-QSAR simulation without needing receptor's active site geometry, resulted in useful descriptors mainly denoting ;C60 backbone-functional groups; and ;C60 functional groups; properties. Both properties in fullerene refer to the ligand fitness and improvement van der Waals interactions with HIV-PR active site. Therefore, the QSAR models can be used in the search for novel HIV-PR inhibitors based on fullerene derivatives.

  3. Review of synthesis, biological assay and QSAR studies of β-secretase inhibitors.

    Science.gov (United States)

    Niño, Helena; García-Pintos, Isela; Rodríguez-Borges, José E; Escobar-Cubiella, Manolo; García-Mera, Xerardo; Prado-Prado, Francisco

    2011-12-01

    Alzheimer's disease (AD) is highly complex. While several pathologies characterize this disease, amyloid plaques, composed of the β-amyloid peptide, are hallmark neuropathological lesions in Alzheimer's disease brain. Indeed, a wealth of evidence suggests that β-amyloid is central to the pathophysiology of AD and is likely to play an early role in this intractable neurodegenerative disorder. The BACE-1 enzyme is essential for the generation of β-amyloid. BACE-1 knockout mice do not produce β-amyloid and are free from Alzheimer's associated pathologies, including neuronal loss and certain memory deficits. The fact that BACE-1 initiates the formation of β-amyloid, and the observation that BACE-1 levels are elevated in this disease provide direct and compelling reasons to develop therapies directed at BACE-1 inhibition, thus reducing β-amyloid and its associated toxicities. In this sense, quantitative structure-activity relationships (QSAR) could play an important role in studying these β-secretase inhibitors. QSAR models are necessary in order to guide the β-secretase synthesis. This work is aimed at reviewing different design and synthesis and computational studies for a very large and heterogeneous series of β-secretase inhibitors. First, we review design, synthesis, and Biological assay of β-secretase inhibitors. Next, we review 2D QSAR, 3D QSAR, CoMFA, CoMSIA and Docking with different compounds to find out the structural requirements. Next, we review QSAR studies using the method of Linear Discriminant Analysis (LDA) in order to understand the essential structural requirement for receptor binding for β- secretase inhibitors.

  4. Synthesis, evaluation and quantitative structure-activity relationship (QSAR) analysis of Wogonin derivatives as cytotoxic agents.

    Science.gov (United States)

    Bian, Jinlei; Li, Tinghan; Weng, Tianwei; Wang, Jubo; Chen, Yu; Li, Zhiyu

    2017-02-15

    A novel series of 49 wogonin derivatives were synthesized by introducing group at 7-, 8- or B ring of wogonin. The cytotoxic activities against HepG2, A549 and BCG-823 cancer cell lines were also investigated in vitro. Several of them showed obvious cytotoxic activities and compound 3h possessed the highest potency against HepG2, A549, and BCG-823 with IC50 values of 1.07μM, 1.74μM and 0.98μM, respectively. A quantitative structure-activity relationship (QSAR) study of these synthetic derivatives as well as wogonin indicated that high solubility and low octanol/water partition coefficient are favorable, and excessive electrostatic properties and refractivity are unfavorable for the cytotoxic activities of these wogonin derivatives. These findings and results provide a base for further investigations. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. Cytotoxicity and 2D-QSAR study of some heterocyclic compounds

    Directory of Open Access Journals (Sweden)

    M. Abul Kashem Liton

    2014-11-01

    Full Text Available Herein we have studied the cytotoxicity and quantitative structure–activity relationship (QSAR of heterocyclic compounds containing cyclic urea and thiourea nuclei. A set of 22 hydantoin and thiohydantoin related heterocyclic compounds were investigated with respect to their LC50 values (Log of LC50 against brine shrimp lethality bioassay in order to derive the 2D-QSAR models using MLR, PLS and ANN methods. The best predictive models by MLR, PLS and ANN methods gave highly significant square correlation coefficient (R2 values of 0.83, 0.81 and 0.91 respectively. The model also exhibited good predictive power confirmed by the high value of cross validated correlation coefficient Q2 (0.74.

  6. Quantitative Structure Activity Relationships (QSAR) for Binary Mixtures at Non-Equitoxic Ratios Based on Toxic Ratios-Effects Curves

    Science.gov (United States)

    Tian, Dayong; Lin, Zhifen; Yin, Daqiang

    2013-01-01

    The present study proposed a QSAR model to predict joint effects at non-equitoxic ratios for binary mixtures containing reactive toxicants, cyanogenic compounds and aldehydes. Toxicity of single and binary mixtures was measured by quantifying the decrease in light emission from the Photobacterium phosphoreum for 15 min. The joint effects of binary mixtures (TUsum) can thus be obtained. The results showed that the relationships between toxic ratios of the individual chemicals and their joint effects can be described by normal distribution function. Based on normal distribution equations, the joint effects of binary mixtures at non-equitoxic ratios ( TUsumn:m) can be predicted quantitatively using the joint effects at equitoxic ratios ( TUsum1:1). Combined with a QSAR model of TUsum1:1in our previous work, a novel QSAR model can be proposed to predict the joint effects of mixtures at non-equitoxic ratios ( TUsumn:m). The proposed model has been validated using additional mixtures other than the one used for the development of the model. Predicted and observed results were similar (p>0.05). This study provides an approach to the prediction of joint effects for binary mixtures at non-equitoxic ratios. PMID:23930105

  7. Synthesis and quantitative structure activity relationship (QSAR) of arylidene (benzimidazol-1-yl)acetohydrazones as potential antibacterial agents.

    Science.gov (United States)

    El-Kilany, Yeldez; Nahas, Nariman M; Al-Ghamdi, Mariam A; Badawy, Mohamed E I; El Ashry, El Sayed H

    2015-01-01

    Ethyl (benzimidazol-1-yl)acetate was subjected to hydrazinolysis with hydrazine hydrate to give (benzimidazol-1-yl)acetohydrazide. The latter was reacted with various aromatic aldehydes to give the respective arylidene (1H-benzimidazol-1-yl)acetohydrazones. Solutions of the prepared hydrazones were found to contain two geometric isomers. Similarly (2-methyl-benzimidazol-1-yl)acetohydrazide was reacted with various aldehydes to give the corresponding hydrazones. The antibacterial activity was evaluated in vitro by minimum inhibitory concentration (MIC) against Agrobacterium tumefaciens (A. tumefaciens), Erwinia carotovora (E. carotovora), Corynebacterium fascians (C. fascians) and Pseudomonas solanacearum (P. solanacearum). MIC result demonstrated that salicylaldehyde(1H-benzimidazol-1-yl)acetohydrazone (4) was the most active compound (MIC = 20, 35, 25 and 30 mg/L against A. tumefaciens, C. fascians, E. carotovora and P. solanacearum, respectively). Quantitative structure activity relationship (QSAR) investigation using Hansch analysis was applied to find out the correlation between antibacterial activity and physicochemical properties. Various physicochemical descriptors and experimentally determined MIC values for different microorganisms were used as independent and dependent variables, respectively. pMICs of the compounds exhibited good correlation (r = 0.983, 0.914, 0.960 and 0.958 for A. tumefaciens, C. fascians, E. carotovora and P. solanacearum, respectively) with the prediction made by the model. QSAR study revealed that the hydrophobic parameter (ClogP), the aqueous solubility (LogS), calculated molar refractivity, topological polar surface area and hydrogen bond acceptor were found to have overall significant correlation with antibacterial activity. The statistical results of training set, correlation coefficient (r and r (2)), the ratio between regression and residual variances (f, Fisher's statistic), the standard error of estimates and

  8. Docking and 3D QSAR Studies on p38α MAP Kinase Inhibitors

    Directory of Open Access Journals (Sweden)

    Mohan Babu Jatavath

    2011-01-01

    Full Text Available The p38 signaling cascade has emerged as an attractive target for the design of novel chemotherapeutic agents for the treatment of inflammatory diseases. Three dimensional quantitative structure- activity relationship (3D- QSAR studies were performed on a series of 25, 2-aminothiazole analogs as inhibitors of p38α mitogen activated protein (MAP kinase. The docking results provided a reliable conformational alignment scheme for the 3D-QSAR model. The 3D-QSAR model showed very good statistical results namely q2, r2 and r2pred values for both comparative molecular field analysis (CoMFA and comparative molecular similarity indices analysis (CoMSIA. The CoMFA and CoMSIA models & docking results provided the most significant correlation of steric, electrostatic, hydrophobic, H-bond donor, H-bond acceptor fields with biological activities and the provided values were in good agreement with the experimental results. The information rendered from molecular modeling studies gave valuable clues to optimize the lead and design new potential inhibitors.

  9. QSAR Study of Skin Sensitization Using Local Lymph Node Assay Data

    Directory of Open Access Journals (Sweden)

    Eugene Demchuk

    2004-01-01

    Full Text Available Abstract: Allergic Contact Dermatitis (ACD is a common work-related skin disease that often develops as a result of repetitive skin exposures to a sensitizing chemical agent. A variety of experimental tests have been suggested to assess the skin sensitization potential. We applied a method of Quantitative Structure-Activity Relationship (QSAR to relate measured and calculated physical-chemical properties of chemical compounds to their sensitization potential. Using statistical methods, each of these properties, called molecular descriptors, was tested for its propensity to predict the sensitization potential. A few of the most informative descriptors were subsequently selected to build a model of skin sensitization. In this work sensitization data for the murine Local Lymph Node Assay (LLNA were used. In principle, LLNA provides a standardized continuous scale suitable for quantitative assessment of skin sensitization. However, at present many LLNA results are still reported on a dichotomous scale, which is consistent with the scale of guinea pig tests, which were widely used in past years. Therefore, in this study only a dichotomous version of the LLNA data was used. To the statistical end, we relied on the logistic regression approach. This approach provides a statistical tool for investigating and predicting skin sensitization that is expressed only in categorical terms of activity and nonactivity. Based on the data of compounds used in this study, our results suggest a QSAR model of ACD that is based on the following descriptors: nDB (number of double bonds, C-003 (number of CHR3 molecular subfragments, GATS6M (autocorrelation coefficient and HATS6m (GETAWAY descriptor, although the relevance of the identified descriptors to the continuous ACD QSAR has yet to be shown. The proposed QSAR model gives a percentage of positively predicted responses of 83% on the training set of compounds, and in cross validation it correctly identifies 79% of

  10. Towards Global QSAR Model Building for Acute Toxicity: Munro Database Case Study

    Directory of Open Access Journals (Sweden)

    Swapnil Chavan

    2014-10-01

    Full Text Available A series of 436 Munro database chemicals were studied with respect to their corresponding experimental LD50 values to investigate the possibility of establishing a global QSAR model for acute toxicity. Dragon molecular descriptors were used for the QSAR model development and genetic algorithms were used to select descriptors better correlated with toxicity data. Toxic values were discretized in a qualitative class on the basis of the Globally Harmonized Scheme: the 436 chemicals were divided into 3 classes based on their experimental LD50 values: highly toxic, intermediate toxic and low to non-toxic. The k-nearest neighbor (k-NN classification method was calibrated on 25 molecular descriptors and gave a non-error rate (NER equal to 0.66 and 0.57 for internal and external prediction sets, respectively. Even if the classification performances are not optimal, the subsequent analysis of the selected descriptors and their relationship with toxicity levels constitute a step towards the development of a global QSAR model for acute toxicity.

  11. 3D-QSAR and molecular docking studies on HIV protease inhibitors

    Science.gov (United States)

    Tong, Jianbo; Wu, Yingji; Bai, Min; Zhan, Pei

    2017-02-01

    In order to well understand the chemical-biological interactions governing their activities toward HIV protease activity, QSAR models of 34 cyclic-urea derivatives with inhibitory HIV were developed. The quantitative structure activity relationship (QSAR) model was built by using comparative molecular similarity indices analysis (CoMSIA) technique. And the best CoMSIA model has rcv2, rncv2 values of 0.586 and 0.931 for cross-validated and non-cross-validated. The predictive ability of CoMSIA model was further validated by a test set of 7 compounds, giving rpred2 value of 0.973. Docking studies were used to find the actual conformations of chemicals in active site of HIV protease, as well as the binding mode pattern to the binding site in protease enzyme. The information provided by 3D-QSAR model and molecular docking may lead to a better understanding of the structural requirements of 34 cyclic-urea derivatives and help to design potential anti-HIV protease molecules.

  12. Advantages and limitations of classic and 3D QSAR approaches in nano-QSAR studies based on biological activity of fullerene derivatives.

    Science.gov (United States)

    Jagiello, Karolina; Grzonkowska, Monika; Swirog, Marta; Ahmed, Lucky; Rasulev, Bakhtiyor; Avramopoulos, Aggelos; Papadopoulos, Manthos G; Leszczynski, Jerzy; Puzyn, Tomasz

    In this contribution, the advantages and limitations of two computational techniques that can be used for the investigation of nanoparticles activity and toxicity: classic nano-QSAR (Quantitative Structure-Activity Relationships employed for nanomaterials) and 3D nano-QSAR (three-dimensional Quantitative Structure-Activity Relationships, such us Comparative Molecular Field Analysis, CoMFA/Comparative Molecular Similarity Indices Analysis, CoMSIA analysis employed for nanomaterials) have been briefly summarized. Both approaches were compared according to the selected criteria, including: efficiency, type of experimental data, class of nanomaterials, time required for calculations and computational cost, difficulties in the interpretation. Taking into account the advantages and limitations of each method, we provide the recommendations for nano-QSAR modellers and QSAR model users to be able to determine a proper and efficient methodology to investigate biological activity of nanoparticles in order to describe the underlying interactions in the most reliable and useful manner.

  13. Advantages and limitations of classic and 3D QSAR approaches in nano-QSAR studies based on biological activity of fullerene derivatives

    Science.gov (United States)

    Jagiello, Karolina; Grzonkowska, Monika; Swirog, Marta; Ahmed, Lucky; Rasulev, Bakhtiyor; Avramopoulos, Aggelos; Papadopoulos, Manthos G.; Leszczynski, Jerzy; Puzyn, Tomasz

    2016-09-01

    In this contribution, the advantages and limitations of two computational techniques that can be used for the investigation of nanoparticles activity and toxicity: classic nano-QSAR (Quantitative Structure-Activity Relationships employed for nanomaterials) and 3D nano-QSAR (three-dimensional Quantitative Structure-Activity Relationships, such us Comparative Molecular Field Analysis, CoMFA/Comparative Molecular Similarity Indices Analysis, CoMSIA analysis employed for nanomaterials) have been briefly summarized. Both approaches were compared according to the selected criteria, including: efficiency, type of experimental data, class of nanomaterials, time required for calculations and computational cost, difficulties in the interpretation. Taking into account the advantages and limitations of each method, we provide the recommendations for nano-QSAR modellers and QSAR model users to be able to determine a proper and efficient methodology to investigate biological activity of nanoparticles in order to describe the underlying interactions in the most reliable and useful manner.

  14. synthesis, screening and qsar studies of 3-benzoyl-2-oxo/thioxo

    African Journals Online (AJOL)

    a

    QSAR studies of antimicrobial activity represent an emerging and exceptionally important topic in the area of computer-aided drug design. QSAR models are highly ..... concentration (MIC) of all these compounds was determined by double dilution method [14]. The biological data minimum inhibitory concentration (MIC) in ...

  15. Synthesis, antimicrobial evaluation and QSAR studies of gallic acid derivatives

    Directory of Open Access Journals (Sweden)

    Anurag Khatkar

    2017-05-01

    Full Text Available A series of gallic acid derivatives (1–33 was synthesized and characterized by physicochemical and spectral means. The synthesized compounds were evaluated in vitro for their antimicrobial activity against different Gram positive and Gram negative bacterial and fungal strains by the tube dilution method. Results of antimicrobial screening indicated that compound 6 was the most active antimicrobial agent (pMICam = 1.92 μM/mL. The results of QSAR studies demonstrated that antibacterial, antifungal and overall antimicrobial activities of synthesized gallic acid derivatives were governed by the electronic parameters, cosmic total energy (Cos E. and nuclear energy (Nu. E..

  16. Benefits of statistical molecular design, covariance analysis, and reference models in QSAR: a case study on acetylcholinesterase.

    Science.gov (United States)

    Andersson, C David; Hillgren, J Mikael; Lindgren, Cecilia; Qian, Weixing; Akfur, Christine; Berg, Lotta; Ekström, Fredrik; Linusson, Anna

    2015-03-01

    Scientific disciplines such as medicinal- and environmental chemistry, pharmacology, and toxicology deal with the questions related to the effects small organic compounds exhort on biological targets and the compounds' physicochemical properties responsible for these effects. A common strategy in this endeavor is to establish structure-activity relationships (SARs). The aim of this work was to illustrate benefits of performing a statistical molecular design (SMD) and proper statistical analysis of the molecules' properties before SAR and quantitative structure-activity relationship (QSAR) analysis. Our SMD followed by synthesis yielded a set of inhibitors of the enzyme acetylcholinesterase (AChE) that had very few inherent dependencies between the substructures in the molecules. If such dependencies exist, they cause severe errors in SAR interpretation and predictions by QSAR-models, and leave a set of molecules less suitable for future decision-making. In our study, SAR- and QSAR models could show which molecular sub-structures and physicochemical features that were advantageous for the AChE inhibition. Finally, the QSAR model was used for the prediction of the inhibition of AChE by an external prediction set of molecules. The accuracy of these predictions was asserted by statistical significance tests and by comparisons to simple but relevant reference models.

  17. QUANTITAVE STRUCTURE-ACTIVITY RELATIONSHIP ANALYSIS (QSAR OF ANTIMALARIAL 1,10-PHENANTHROLINE DERIVATIVES COMPOUNDS

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    Ruslin Hadanu

    2010-06-01

    Full Text Available Quantitative Electronic Structure-Activity Relationship (QSAR analysis of a series of 1,10-phenanthroline derivatives as antiplasmodial compounds have been conducted using atomic net charges (q, dipole moment (μ ELUMO, EHOMO, polarizability (α and log P as the descriptors. The descriptors were obtained from computational chemistry method using semi-empirical PM3. Antiplasmodial activities were taken as the activity of the drugs  against  chloroquine-resistant Plasmodium falciparum FCR3 strain and are presented as the value of ln (1/IC50 where IC50 is an effective concentration inhibiting 50% of the parasite growth. The best model of QSAR model was determine by multiple linear regression method and giving equation of QSAR: ln 1/IC50  =  3.732 + (5.098 qC5 + (7.051 qC7 + (36.696 qC9 + (41.467 qC11 -(135.497 qC12 + (0.332 μ -                    (0.170 α + (0.757 log P. The equation was significant on the 95% level with statistical parameters: n=16; r=0.987; r2= 0.975; SE=0.317;  Fcalc/Ftable = 15.337 and gave the PRESS=0.707. Its means that there were only a relatively few deviations between the experimental and theoretical data of antimalarial activity.   Keywords: QSAR, antimalarial, semi-empirical method, 1,10-phenanthroline.

  18. Design, synthesis and 3D-QSAR study of cytotoxic flavonoid derivatives.

    Science.gov (United States)

    Ou, Lili; Han, Shuang; Ding, Wenbo; Chen, Zhe; Ye, Ziqi; Yang, Hongyu; Zhang, Goulin; Lou, Yijia; Chen, Jian-Zhong; Yu, Yongping

    2011-08-01

    Three series of flavonoid derivatives were designed and synthesized. All synthesized compounds were evaluated for cytotoxic activities against five human cancer cell lines, including K562, PC-3, MCF-7, A549, and HO8910. Among the compounds tested, compound 9 d exhibited the most potent cytotoxic activity with IC(50) values of 2.76-6.98 μM. Further comparative molecular field analysis was performed to conduct a 3D quantitative structure-activity relationship study. The generated 3D-QSAR model could be used for further rational design of novel flavonoid analogs as highly potent cytotoxic agents.

  19. 2D and 3D-QSAR studies on antiproliferative thiazolidine analogs

    Science.gov (United States)

    Liao, Si Yan; Qian, Li; Chen, Jin Can; Lu, Hai Liang; Zheng, Kang Cheng

    Two-dimensional (2D) and three-dimensional (3D) quantitative structure-activity relationships (QSARs) of 22 thiazolidine analogs with antiproliferative activity expressed as pIC50, which is defined as the negative value of the logarithm of necessary molar concentration of these compounds to cause 50% growth inhibition against melanoma cell lines WM-164, have been studied by using a combined method of the DFT, MM2 and statistics for 2D, as well as the comparative molecular field analysis (CoMFA) method for 3D. The established 2D-QSAR model in training set comprised of random 18 compounds shows not only significant statistical quality, but also predictive ability, with the square of adjusted correlation coefficient (R2A = 0.832) and the square of the cross-validation coefficient (q2 = 0.803). The same model was further applied to predict pIC50 values of the four compounds in the test set, and the resulting R2pred reaching 0.784, further confirms that this 2D-QSAR model has high predictive ability. The 3D-QSAR model also shows good correlative and predictive capabilities in terms of R2 (0.956) and q2 (0.615) obtained from CoMFA model. Further, the robustness of the CoMFA model was verified by bootstrapping analysis (100 runs) with R2bs (0.979) and SDbs (0.056). It is very interesting to find that the results from 2D- and 3D-QSAR analyses accord with each other, and they all show that the steric interaction plays a crucial role in determining the cytotoxicities of the compounds, and that selecting a moderate-size or appropriate-hydrophobicity substituent R as well as increasing the negative charges of C4 on phenyl ring at the same time are advantageous to improving the cytotoxicity. Such results can offer some useful theoretical references for directing the molecular design and understanding the action mechanism of this kind of compound with antiproliferative activity.

  20. ANTIBACTERIAL ACTIVITIES, DFT AND QSAR STUDIES OF ...

    African Journals Online (AJOL)

    The nonlinear optical (NLO) properties vary by changing the theory (DFT to HF) or functional. (B3LYP to CAM-B3LYP). .... HOMO energy (EHOMO), LUMO energy (ELUMO) and HOMO–LUMO energy gap (Egap) in eV, absorption wavelengths (λa)* in nm of studied compounds. System. EHOMO. ELUMO. Egap. Egap λa a λa.

  1. Novel 1,4-naphthoquinone-based sulfonamides: Synthesis, QSAR, anticancer and antimalarial studies.

    Science.gov (United States)

    Pingaew, Ratchanok; Prachayasittikul, Veda; Worachartcheewan, Apilak; Nantasenamat, Chanin; Prachayasittikul, Supaluk; Ruchirawat, Somsak; Prachayasittikul, Virapong

    2015-10-20

    A novel series of 1,4-naphthoquinones (33-44) tethered by open and closed chain sulfonamide moieties were designed, synthesized and evaluated for their cytotoxic and antimalarial activities. All quinone-sulfonamide derivatives displayed a broad spectrum of cytotoxic activities against all of the tested cancer cell lines including HuCCA-1, HepG2, A549 and MOLT-3. Most quinones (33-36 and 38-43) exerted higher anticancer activity against HepG2 cell than that of the etoposide. The open chain analogs 36 and 42 were shown to be the most potent compounds. Notably, the restricted sulfonamide analog 38 with 6,7-dimethoxy groups exhibited the most potent antimalarial activity (IC₅₀ = 2.8 μM). Quantitative structure-activity relationships (QSAR) study was performed to reveal important chemical features governing the biological activities. Five constructed QSAR models provided acceptable predictive performance (Rcv 0.5647-0.9317 and RMSEcv 0.1231-0.2825). Four additional sets of structurally modified compounds were generated in silico (34a-34d, 36a-36k, 40a-40d and 42a-42k) in which their activities were predicted using the constructed QSAR models. A comprehensive discussion of the structure-activity relationships was made and a set of promising compounds (i.e., 33, 36, 38, 42, 36d, 36f, 42e, 42g and 42f) was suggested for further development as anticancer and antimalarial agents. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

  2. A DFT-based toxicity QSAR study of aromatic hydrocarbons to Vibrio fischeri: Consideration of aqueous freely dissolved concentration.

    Science.gov (United States)

    Wang, Ying; Yang, Xianhai; Wang, Juying; Cong, Yi; Mu, Jingli; Jin, Fei

    2016-05-05

    In the present study, quantitative structure-activity relationship (QSAR) techniques based on toxicity mechanism and density functional theory (DFT) descriptors were adopted to develop predictive models for the toxicity of alkylated and parent aromatic hydrocarbons to Vibrio fischeri. The acute toxicity data of 17 aromatic hydrocarbons from both literature and our experimental results were used to construct QSAR models by partial least squares (PLS) analysis. With consideration of the toxicity process, the partition of aromatic hydrocarbons between water phase and lipid phase and their interaction with the target biomolecule, the optimal QSAR model was obtained by introducing aqueous freely dissolved concentration. The high statistical values of R(2) (0.956) and Q(CUM)(2) (0.942) indicated that the model has good goodness-of-fit, robustness and internal predictive power. The average molecular polarizability (α) and several selected thermodynamic parameters reflecting the intermolecular interactions played important roles in the partition of aromatic hydrocarbons between the water phase and biomembrane. Energy of the highest occupied molecular orbital (E(HOMO)) was the most influential descriptor which dominated the toxicity of aromatic hydrocarbons through the electron-transfer reaction with biomolecules. The results demonstrated that the adoption of freely dissolved concentration instead of nominal concentration was a beneficial attempt for toxicity QSAR modeling of hydrophobic organic chemicals. Copyright © 2016 Elsevier B.V. All rights reserved.

  3. Synthesis, antimicrobial evaluation and QSAR studies of propionic acid derivatives

    Directory of Open Access Journals (Sweden)

    Sanjiv Kumar

    2017-02-01

    Full Text Available A series of Schiff bases (1–17 and esters (18–24 of propionic acid was synthesized in appreciable yield and characterized by physicochemical as well as spectral means. The synthesized compounds were evaluated in vitro for their antimicrobial activity against Gram-positive bacteria Staphylococcus aureus, Bacillus subtilis, Gram negative bacterium Escherichia coli and fungal strains Candida albicans and Aspergillus niger by tube dilution method. Results of antimicrobial screening indicated that besides having good antibacterial activity, the synthesized compounds also displayed appreciable antifungal activity and compound 10 emerged as the most active antifungal agent (pMICca and pMICan = 1.93. The results of QSAR studies demonstrated that antibacterial, antifungal and overall antimicrobial activities of synthesized propionic acid derivatives were governed by the topological parameters, Kier’s alpha first order shape index (κα1 and valence first order molecular connectivity index (1χv.

  4. Molecular docking, QSAR and ADMET studies of withanolide analogs against breast cancer

    Science.gov (United States)

    Yadav, Dharmendra K; Kumar, Surendra; Saloni; Singh, Harpreet; Kim, Mi-hyun; Sharma, Praveen; Misra, Sanjeev; Khan, Feroz

    2017-01-01

    Withanolides are a group of pharmacologically active compounds present in most prodigal amounts in roots and leaves of Withania somnifera (Indian ginseng), one of the most important medicinal plants of Indian traditional practice of medicine. Withanolides are steroidal lactones (highly oxygenated C-28 phytochemicals) and have been reported to exhibit immunomodulatory, anticancer and other activities. In the present study, a quantitative structure activity relationship (QSAR) model was developed by a forward stepwise multiple linear regression method to predict the activity of withanolide analogs against human breast cancer. The most effective QSAR model for anticancer activity against the SK-Br-3 cell showed the best correlation with activity (r2=0.93 and rCV2 =0.90). Similarly, cross-validation regression coefficient (rCV2=0.85) of the best QSAR model against the MCF7/BUS cells showed a high correlation (r2=0.91). In particular, compounds CID_73621, CID_435144, CID_301751 and CID_3372729 have a marked antiproliferative activity against the MCF7/BUS cells, while 2,3-dihydrowithaferin A-3-beta-O-sulfate, withanolide 5, withanolide A, withaferin A, CID_10413139, CID_11294368, CID_53477765, CID_135887, CID_301751 and CID_3372729 have a high activity against the Sk-Br-3 cells compared to standard drugs 5-fluorouracil (5-FU) and camptothecin. Molecular docking was performed to study the binding conformations and different bonding behaviors, in order to reveal the plausible mechanism of action behind higher accumulation of active withanolide analogs with β-tubulin. The results of the present study may help in the designing of lead compound with improved activity. PMID:28694686

  5. QSAR Study for Carcinogenic Potency of Aromatic Amines Based on GEP and MLPs

    Directory of Open Access Journals (Sweden)

    Fucheng Song

    2016-11-01

    Full Text Available A new analysis strategy was used to classify the carcinogenicity of aromatic amines. The physical-chemical parameters are closely related to the carcinogenicity of compounds. Quantitative structure activity relationship (QSAR is a method of predicting the carcinogenicity of aromatic amine, which can reveal the relationship between carcinogenicity and physical-chemical parameters. This study accessed gene expression programming by APS software, the multilayer perceptrons by Weka software to predict the carcinogenicity of aromatic amines, respectively. All these methods relied on molecular descriptors calculated by CODESSA software and eight molecular descriptors were selected to build function equations. As a remarkable result, the accuracy of gene expression programming in training and test sets are 0.92 and 0.82, the accuracy of multilayer perceptrons in training and test sets are 0.84 and 0.74 respectively. The precision of the gene expression programming is obviously superior to multilayer perceptrons both in training set and test set. The QSAR application in the identification of carcinogenic compounds is a high efficiency method.

  6. Insights on Cytochrome P450 Enzymes and Inhibitors Obtained Through QSAR Studies

    Directory of Open Access Journals (Sweden)

    Maryam Foroozesh

    2012-08-01

    Full Text Available The cytochrome P450 (CYP superfamily of heme enzymes play an important role in the metabolism of a large number of endogenous and exogenous compounds, including most of the drugs currently on the market. Inhibitors of CYP enzymes have important roles in the treatment of several disease conditions such as numerous cancers and fungal infections in addition to their critical role in drug-drug interactions. Structure activity relationships (SAR, and three-dimensional quantitative structure activity relationships (3D-QSAR represent important tools in understanding the interactions of the inhibitors with the active sites of the CYP enzymes. A comprehensive account of the QSAR studies on the major human CYPs 1A1, 1A2, 1B1, 2A6, 2B6, 2C9, 2C19, 2D6, 2E1, 3A4 and a few other CYPs are detailed in this review which will provide us with an insight into the individual/common characteristics of the active sites of these enzymes and the enzyme-inhibitor interactions.

  7. QSAR Study for Carcinogenic Potency of Aromatic Amines Based on GEP and MLPs

    Science.gov (United States)

    Song, Fucheng; Zhang, Anling; Liang, Hui; Cui, Lianhua; Li, Wenlian; Si, Hongzong; Duan, Yunbo; Zhai, Honglin

    2016-01-01

    A new analysis strategy was used to classify the carcinogenicity of aromatic amines. The physical-chemical parameters are closely related to the carcinogenicity of compounds. Quantitative structure activity relationship (QSAR) is a method of predicting the carcinogenicity of aromatic amine, which can reveal the relationship between carcinogenicity and physical-chemical parameters. This study accessed gene expression programming by APS software, the multilayer perceptrons by Weka software to predict the carcinogenicity of aromatic amines, respectively. All these methods relied on molecular descriptors calculated by CODESSA software and eight molecular descriptors were selected to build function equations. As a remarkable result, the accuracy of gene expression programming in training and test sets are 0.92 and 0.82, the accuracy of multilayer perceptrons in training and test sets are 0.84 and 0.74 respectively. The precision of the gene expression programming is obviously superior to multilayer perceptrons both in training set and test set. The QSAR application in the identification of carcinogenic compounds is a high efficiency method. PMID:27854309

  8. QSAR study of HCV NS5B polymerase inhibitors using the genetic algorithm-multiple linear regression (GA-MLR)

    OpenAIRE

    Rafiei, Hamid; Khanzadeh, Marziyeh; Mozaffari, Shahla; Bostanifar, Mohammad Hassan; Avval, Zhila Mohajeri; Aalizadeh, Reza; Pourbasheer, Eslam

    2016-01-01

    Quantitative structure-activity relationship (QSAR) study has been employed for predicting the inhibitory activities of the Hepatitis C virus (HCV) NS5B polymerase inhibitors . A data set consisted of 72 compounds was selected, and then different types of molecular descriptors were calculated. The whole data set was split into a training set (80 % of the dataset) and a test set (20 % of the dataset) using principle component analysis. The stepwise (SW) and the genetic algorithm (GA) technique...

  9. 2D-QSAR study of some 2,5-diaminobenzophenone farnesyltransferase inhibitors by different chemometric methods

    OpenAIRE

    Ghanbarzadeh, Saeed; Ghasemi, Saeed; Shayanfar, Ali; Ebrahimi-Najafabadi, Heshmatollah

    2015-01-01

    Quantitative structure activity relationship (QSAR) models can be used to predict the activity of new drug candidates in early stages of drug discovery. In the present study, the information of the ninety two 2,5-diaminobenzophenone-containing farnesyltranaferase inhibitors (FTIs) were taken from the literature. Subsequently, the structures of the molecules were optimized using Hyperchem software and molecular descriptors were obtained using Dragon software. The most suitable descriptors were...

  10. 3D-QSAR Studies on a Series of Dihydroorotate Dehydrogenase Inhibitors: Analogues of the Active Metabolite of Leflunomide

    OpenAIRE

    Li, Shun-Lai; He, Mao-Yu; Du, Hong-Guang

    2011-01-01

    The active metabolite of the novel immunosuppressive agent leflunomide has been shown to inhibit the enzyme dihydroorotate dehydrogenase (DHODH). This enzyme catalyzes the fourth step in de novo pyrimidine biosynthesis. Self-organizing molecular field analysis (SOMFA), a simple three-dimensional quantitative structure-activity relationship (3D-QSAR) method is used to study the correlation between the molecular properties and the biological activities of a series of analogues of the active met...

  11. Molecular docking, QSAR and ADMET studies of withanolide analogs against breast cancer

    Directory of Open Access Journals (Sweden)

    Yadav DK

    2017-06-01

    Full Text Available Dharmendra K Yadav,1 Surendra Kumar,2 Saloni,1 Harpreet Singh,3 Mi-hyun Kim,1 Praveen Sharma,4 Sanjeev Misra,4 Feroz Khan5 1Department of Pharmacy, College of Pharmacy, Gachon University, Yeonsu-gu, Incheon, Republic of Korea; 2Faculty of Pharmacy, Department of Pharmaceutical Chemistry, Babu Banarasi Das Northern India Institute of Technology, Lucknow, 3Department of Bioinformatics, Indian Council of Medical Research, New Delhi, 4Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, 5Metabolic & Structural Biology Department, CSIR– Central Institute of Medicinal & Aromatic Plant, Lucknow, India Abstract: Withanolides are a group of pharmacologically active compounds present in most prodigal amounts in roots and leaves of Withania somnifera (Indian ginseng, one of the most important medicinal plants of Indian traditional practice of medicine. Withanolides are steroidal lactones (highly oxygenated C-28 phytochemicals and have been reported to exhibit immunomodulatory, anticancer and other activities. In the present study, a quantitative structure activity relationship (QSAR model was developed by a forward stepwise multiple linear regression method to predict the activity of withanolide analogs against human breast cancer. The most effective QSAR model for anticancer activity against the SK-Br-3 cell showed the best correlation with activity (r2=0.93 and rCV2 =0.90. Similarly, cross-validation regression coefficient (rCV2=0.85 of the best QSAR model against the MCF7/BUS cells showed a high correlation (r2=0.91. In particular, compounds CID_73621, CID_435144, CID_301751 and CID_3372729 have a marked antiproliferative activity against the MCF7/BUS cells, while 2,3-dihydrowithaferin A-3-beta-O-sulfate, withanolide 5, withanolide A, withaferin A, CID_10413139, CID_11294368, CID_53477765, CID_135887, CID_301751 and CID_3372729 have a high activity against the Sk-Br-3 cells compared to standard drugs 5-fluorouracil (5-FU and

  12. 3D-QSAR and docking studies of flavonoids as potent Escherichia coli inhibitors

    Science.gov (United States)

    Fang, Yajing; Lu, Yulin; Zang, Xixi; Wu, Ting; Qi, XiaoJuan; Pan, Siyi; Xu, Xiaoyun

    2016-01-01

    Flavonoids are potential antibacterial agents. However, key substituents and mechanism for their antibacterial activity have not been fully investigated. The quantitative structure-activity relationship (QSAR) and molecular docking of flavonoids relating to potent anti-Escherichia coli agents were investigated. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were developed by using the pIC50 values of flavonoids. The cross-validated coefficient (q2) values for CoMFA (0.743) and for CoMSIA (0.708) were achieved, illustrating high predictive capabilities. Selected descriptors for the CoMFA model were ClogP (logarithm of the octanol/water partition coefficient), steric and electrostatic fields, while, ClogP, electrostatic and hydrogen bond donor fields were used for the CoMSIA model. Molecular docking results confirmed that half of the tested flavonoids inhibited DNA gyrase B (GyrB) by interacting with adenosine-triphosphate (ATP) pocket in a same orientation. Polymethoxyl flavones, flavonoid glycosides, isoflavonoids changed their orientation, resulting in a decrease of inhibitory activity. Moreover, docking results showed that 3-hydroxyl, 5-hydroxyl, 7-hydroxyl and 4-carbonyl groups were found to be crucial active substituents of flavonoids by interacting with key residues of GyrB, which were in agreement with the QSAR study results. These results provide valuable information for structure requirements of flavonoids as antibacterial agents. PMID:27049530

  13. QSAR study of some pyrazolo[3,4-d]pyrimidine derivatives as the c-Src inhibitors

    Science.gov (United States)

    Shukla, Bindesh Kumar; Yadava, Umesh

    2016-05-01

    Two dimensional quantitative structure activity relationship (QSAR) studies have been carried out on a series of 42 pyrazolo[3,4-d]pyrimidine derivatives to find out the structural requirements for the inhibition of c-SRC phosphorilation. The best predictions were obtained using Heuristic and Best MLR methods from the model where 33 compounds were considered in the training set and the remaining 9 in the test set. Both Best MLR and Heuristic methods indicate that squared correlation coefficient for training and test sets are very close to observed biological activities which designate the good correlation between the experimental and predicted activity. The results that are obtained from 2D-QSAR studies may provide useful insights into the roles of various substitution patterns on the pyrazolo[3,4-d]pyrimidine core and may also help to design more potent compounds.

  14. Combined 2D and 3D-QSAR, molecular modelling and docking studies of pyrazolodiazepinones as novel phosphodiesterase 2 inhibitors.

    Science.gov (United States)

    Bhansali, S G; Kulkarni, V M

    2014-01-01

    Selective inhibition of phosphodiesterase 2 (PDE2) in cells where it is located elevates cyclic guanosine monophosphate (cGMP) and acts as novel analgesic with antinociceptive activity. Three-dimensional quantitative structure-activity relationship (QSAR) studies for pyrazolodiazepinone inhibitors exhibiting PDE2 inhibition were performed using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) and Topomer CoMFA, and two-dimensional QSAR study was performed using a Hologram QSAR (HQSAR) method. QSAR models were generated using training set of 23 compounds and were validated using test set of nine compounds. The optimum partial least squares (PLS) for CoMFA-Focusing, CoMSIA-SDH, Topomer CoMFA and HQSAR models exhibited good 'leave-one-out' cross validated correlation coefficient (q(2)) of 0.790, 0.769, 0.840 and 0.787, coefficient of determination (r(2)) of 0.999, 0.964, 0.979 and 0.980, and high predictive power (r(2)(pred)) of 0.796, 0.833, 0.820 and 0.803 respectively. Docking studies revealed that those inhibitors able to bind to amino acid Gln859 by cGMP binding orientation called 'glutamine-switch', and also bind to the hydrophobic clamp of PDE2 isoform, could possess high selectivity for PDE2. From the results of all the studies, structure-activity relationships and structural requirements for binding to active site of PDE2 were established which provide useful guidance for the design and future synthesis of potent PDE2 inhibitors.

  15. Combined Structure-Based Pharmacophore and 3D-QSAR Studies on Phenylalanine Series Compounds as TPH1 Inhibitors

    Directory of Open Access Journals (Sweden)

    Mingli Xiang

    2012-05-01

    Full Text Available Tryptophan hydroxylase-1 (TPH1 is a key enzyme in the synthesis of serotonin. As a neurotransmitter, serotonin plays important physiological roles both peripherally and centrally. In this study, a combination of ligand-based and structure-based methods is used to clarify the essential quantitative structure-activity relationship (QSAR of known TPH1 inhibitors. A multicomplex-based pharmacophore (MCBP guided method has been suggested to generate a comprehensive pharmacophore of TPH1 kinase based on three crystal structures of TPH1-inhibitor complex. This model has been successfully used to identify the bioactive conformation and align 32 structurally diverse substituted phenylalanine derivatives. The QSAR analyses have been performed on these TPH1 inhibitors based on the MCBP guided alignment. These results may provide important information for further design and virtual screening of novel TPH1 inhibitors.

  16. Molecular modeling studies on benzimidazole carboxamide derivatives as PARP-1 inhibitors using 3D-QSAR and docking.

    Science.gov (United States)

    Zeng, Huahui; Zhang, Huabei; Jang, Fubin; Zhao, Lingzhou; Zhang, Jianyuan

    2011-09-01

    Poly(ADP-ribose) polymerases (PARPs) play significant roles in various cellular functions including DNA repair and control of RNA transcription. PARP-1 inhibitors have been demonstrated to potentiate the effect of cytotoxic agents or radiation in a number of animal tumor models. To understand the structure-activity correlation of cyclic amine-containing benzimidazole carboxamide-based PARP-1 inhibitors, we have carried out a combined molecular docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling study. Two types of satisfactory substructure-based 3D-QSAR models were built, including the comparative molecular field analysis (CoMFA) model (r(2) , 0.913; q(2) , 0.743) and comparative molecular similarity indices analysis (CoMSIA) model (r(2) , 0.869; q(2) , 0.734), to predict the biologic activity of new compounds. Docking studies were performed to explore the binding mode between all of the inhibitors and the PARP-1 and produce the bioactive conformation of each compound in the whole data set. The docked conformer-based alignment strategy gave the best 3D-QSAR models, CoMFA model (r(2) , 0.899; q(2) , 0.712) and CoMSIA model (r(2) , 0.889; q(2) , 0.744), respectively. The structural insights obtained from both the 3D-QSAR contour maps and molecular docking help to better interpret the structure-activity relationship. The information obtained from molecular modeling studies helped us to predict the activity of new inhibitors and further design some novel and potent PARP-1 enzyme inhibitors. © 2011 John Wiley & Sons A/S.

  17. A comparative QSAR study on the estrogenic activities of persistent organic pollutants by PLS and SVM

    Directory of Open Access Journals (Sweden)

    Fei Li

    2015-11-01

    Full Text Available Quantitative structure-activity relationships (QSARs were determined using partial least square (PLS and support vector machine (SVM. The predicted values by the final QSAR models were in good agreement with the corresponding experimental values. Chemical estrogenic activities are related to atomic properties (atomic Sanderson electronegativities, van der Waals volumes and polarizabilities. Comparison of the results obtained from two models, the SVM method exhibited better overall performances. Besides, three PLS models were constructed for some specific families based on their chemical structures. These predictive models should be useful to rapidly identify potential estrogenic endocrine disrupting chemicals.

  18. Multiple receptor conformation docking, dock pose clustering and 3D QSAR studies on human poly(ADP-ribose) polymerase-1 (PARP-1) inhibitors.

    Science.gov (United States)

    Fatima, Sabiha; Jatavath, Mohan Babu; Bathini, Raju; Sivan, Sree Kanth; Manga, Vijjulatha

    2014-10-01

    Poly(ADP-ribose) polymerase-1 (PARP-1) functions as a DNA damage sensor and signaling molecule. It plays a vital role in the repair of DNA strand breaks induced by radiation and chemotherapeutic drugs; inhibitors of this enzyme have the potential to improve cancer chemotherapy or radiotherapy. Three-dimensional quantitative structure activity relationship (3D QSAR) models were developed using comparative molecular field analysis, comparative molecular similarity indices analysis and docking studies. A set of 88 molecules were docked into the active site of six X-ray crystal structures of poly(ADP-ribose)polymerase-1 (PARP-1), by a procedure called multiple receptor conformation docking (MRCD), in order to improve the 3D QSAR models through the analysis of binding conformations. The docked poses were clustered to obtain the best receptor binding conformation. These dock poses from clustering were used for 3D QSAR analysis. Based on MRCD and QSAR information, some key features have been identified that explain the observed variance in the activity. Two receptor-based QSAR models were generated; these models showed good internal and external statistical reliability that is evident from the [Formula: see text], [Formula: see text] and [Formula: see text]. The identified key features enabled us to design new PARP-1 inhibitors.

  19. QSAR Modeling: Where have you been? Where are you going to?

    Science.gov (United States)

    Cherkasov, Artem; Muratov, Eugene N.; Fourches, Denis; Varnek, Alexandre; Baskin, Igor I.; Cronin, Mark; Dearden, John; Gramatica, Paola; Martin, Yvonne C.; Todeschini, Roberto; Consonni, Viviana; Kuz'min, Victor E.; Cramer, Richard; Benigni, Romualdo; Yang, Chihae; Rathman, James; Terfloth, Lothar; Gasteiger, Johann; Richard, Ann; Tropsha, Alexander

    2014-01-01

    Quantitative Structure-Activity Relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss: (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists towards collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making. PMID:24351051

  20. Biological Evaluation and 3D-QSAR Studies of Curcumin Analogues as Aldehyde Dehydrogenase 1 Inhibitors

    Directory of Open Access Journals (Sweden)

    Hui Wang

    2014-05-01

    Full Text Available Aldehyde dehydrogenase 1 (ALDH1 is reported as a biomarker for identifying some cancer stem cells, and down-regulation or inhibition of the enzyme can be effective in anti-drug resistance and a potent therapeutic for some tumours. In this paper, the inhibitory activity, mechanism mode, molecular docking and 3D-QSAR (three-dimensional quantitative structure activity relationship of curcumin analogues (CAs against ALDH1 were studied. Results demonstrated that curcumin and CAs possessed potent inhibitory activity against ALDH1, and the CAs compound with ortho di-hydroxyl groups showed the most potent inhibitory activity. This study indicates that CAs may represent a new class of ALDH1 inhibitor.

  1. Unsupervised selection of informative descriptors in QSAR study of anti-HIV activities of HEPT derivatives

    DEFF Research Database (Denmark)

    Bagheri, Saeed; Omidikia, Nematollah; Kompany-Zareh, Mohsen

    2013-01-01

    selection is unsupervised indeed. Besides, scores that are the linear combination of the data variables are set as dependent variables (artificial dependent variables). It includes 107 derivatives of HEPT molecule, characterized by 160 descriptors encoding the steric, hydrophobic, electronic and structural......With the increasing ease of measuring and calculating multiple descriptors per molecule in quantitative structure-activity relationship, the importance of variable selection for data reduction and improving interpretability is gaining importance. While variable selection has been extensively...... studied in the context of supervised learning, in this paper, an unsupervised learning method is proposed for variable selection and its performance is assessed using a typical QSAR data set. Whereas there is no real dependent variable in the proposed variable selection algorithm, applied variable...

  2. Are Mechanistic and Statistical QSAR Approaches Really Different? MLR Studies on 158 Cycloalkyl-Pyranones.

    Science.gov (United States)

    Bhhatarai, Barun; Garg, Rajni; Gramatica, Paola

    2010-07-12

    Two parallel approaches for quantitative structure-activity relationships (QSAR) are predominant in literature, one guided by mechanistic methods (including read-across) and another by the use of statistical methods. To bridge the gap between these two approaches and to verify their main differences, a comparative study of mechanistically relevant and statistically relevant QSAR models, developed on a case study of 158 cycloalkyl-pyranones, biologically active on inhibition (Ki ) of HIV protease, was performed. Firstly, Multiple Linear Regression (MLR) based models were developed starting from a limited amount of molecular descriptors which were widely proven to have mechanistic interpretation. Then robust and predictive MLR models were developed on the same set using two different statistical approaches unbiased of input descriptors. Development of models based on Statistical I method was guided by stepwise addition of descriptors while Genetic Algorithm based selection of descriptors was used for the Statistical II. Internal validation, the standard error of the estimate, and Fisher's significance test were performed for both the statistical models. In addition, external validation was performed for Statistical II model, and Applicability Domain was verified as normally practiced in this approach. The relationships between the activity and the important descriptors selected in all the models were analyzed and compared. It is concluded that, despite the different type and number of input descriptors, and the applied descriptor selection tools or the algorithms used for developing the final model, the mechanistical and statistical approach are comparable to each other in terms of quality and also for mechanistic interpretability of modelling descriptors. Agreement can be observed between these two approaches and the better result could be a consensus prediction from both the models. Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  3. QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP ANALYSIS (QSAR OF VINCADIFFORMINE ANALOGUES AS THE ANTIPLASMODIAL COMPOUNDS OF THE CHLOROQUINOSENSIBLE STRAIN

    Directory of Open Access Journals (Sweden)

    Iqmal Tahir

    2010-06-01

    Full Text Available Quantitative Structure-Activity Relationship (QSAR analysis of vincadifformine analogs as an antimalarial drug has been conducted using atomic net charges (q, moment dipole (, LUMO (Lowest Unoccupied Molecular Orbital and HOMO (Highest Occupied Molecular Orbital energies, molecular mass (m as well as surface area (A as the predictors to their activity. Data of predictors are obtained from computational chemistry method using semi-empirical molecular orbital AM1 calculation. Antimalarial activities were taken as the activity of the drugs against chloroquine-sensitive Plasmodium falciparum (Nigerian Cell strain and were presented as the value of ln(1/IC50 where IC50 is an effective concentration inhibiting 50% of the parasite growth. The best QSAR model has been determined by multiple linier regression analysis giving QSAR equation: Log (1/IC50 = 9.602.qC1 -17.012.qC2 +6.084.qC3 -19.758.qC5 -6.517.qC6 +2.746.qC7 -6.795.qN +6.59.qC8 -0.190. -0.974.ELUMO +0.515.EHOMO -0.274. +0.029.A -1.673 (n = 16; r = 0.995; SD = 0.099; F = 2.682   Keywords: QSAR analysis, antimalaria, vincadifformine.

  4. Prediction of Acute Mammalian Toxicity Using QSAR Methods: A Case Study of Sulfur Mustard and Its Breakdown Products

    Directory of Open Access Journals (Sweden)

    John Wheeler

    2012-07-01

    Full Text Available Predicting toxicity quantitatively, using Quantitative Structure Activity Relationships (QSAR, has matured over recent years to the point that the predictions can be used to help identify missing comparison values in a substance’s database. In this manuscript we investigate using the lethal dose that kills fifty percent of a test population (the LD50 for determining relative toxicity of a number of substances. In general, the smaller the LD50 value, the more toxic the chemical, and the larger the LD50 value, the lower the toxicity. When systemic toxicity and other specific toxicity data are unavailable for the chemical(s of interest, during emergency responses, LD50 values may be employed to determine the relative toxicity of a series of chemicals. In the present study, a group of chemical warfare agents and their breakdown products have been evaluated using four available rat oral QSAR LD50 models. The QSAR analysis shows that the breakdown products of Sulfur Mustard (HD are predicted to be less toxic than the parent compound as well as other known breakdown products that have known toxicities. The QSAR estimated break down products LD50 values ranged from 299 mg/kg to 5,764 mg/kg. This evaluation allows for the ranking and toxicity estimation of compounds for which little toxicity information existed; thus leading to better risk decision making in the field.

  5. The importance of data curation on QSAR Modeling - PHYSPROP open data as a case study. (QSAR 2016)

    Science.gov (United States)

    During the last few decades many QSAR models and tools have been developed at the US EPA, including the widely used EPISuite. During this period the arsenal of computational capabilities supporting cheminformatics has broadened dramatically with multiple software packages. These ...

  6. QSAR study and conformational analysis of 4-arylthiazolylhydrazones derived from 1-indanones with anti-Trypanosoma cruzi activity.

    Science.gov (United States)

    Noguera, Guido J; Fabian, Lucas E; Lombardo, Elisa; Finkielsztein, Liliana

    2015-10-12

    A set of 4-arylthiazolylhydrazones derived from 1-indanones (TZHs) previously synthesized and assayed against Trypanosoma cruzi, the causative agent of Chagas disease, were explored in terms of conformational analysis. We found that TZHs can adopt four minimum energy conformations: cis (A, B and C) and trans. The possible bioactive conformation was selected by a 3D-QSAR model. Different molecular parameters were calculated to produce QSAR second-generation models. These QSAR results are discussed in conjunction with conformational analysis from molecular modeling studies. The main factor to determine the activity of the compounds was the partial charge at the N(3) atom (qN3). The predictive ability of the QSAR equations proposed was experimentally validated. The QSAR models developed in this study will be helpful to design novel potent TZHs. Copyright © 2015 Elsevier B.V. All rights reserved.

  7. QSAR study for anti-HIV-1 activities of HEPT derivatives using MLR and PLS

    Directory of Open Access Journals (Sweden)

    Ivan Daniela

    2013-01-01

    Full Text Available A QSAR study using Multiple Linear Regression (MLR and a Partial Least Squares (PLS methodology was performed for a series of 127 derivatives of 1-(2-hydroxy-ethoxymethyl]-6-(phenylthio-timine (HEPT, a potent inhibitor of the of the human immunodeficiency virus type 1, HIV-1 reverse transcriptase (RT. To explore the relationship between a pool of HEPT derivative descriptors (as independent variables and anti-HIV-1 activity expressed as log (1/EC50, as dependent variable MLR and PLS methods have been employed. Using Dragon descriptors, the present study aims to develop a predictive and robust QSAR model for predicting anti-HIV activity of the HEPT derivatives for better understanding the molecular features of these compounds important for their biological activity. According to the squared correlation coefficients, which had values between 0.826 and 0.809 for the MLR and PLS methods, the results demonstrate almost identical qualities and good predictive ability for both MLR and PLS models. After dividing the dataset into training and test sets, the model predictability was tested by several parameters, including the Golbraikh-Tropsha external criteria and the goodness of fit tested with the Y-randomization test. [Acknowledgements. This project was financially supported by Project 1.1 and 1.2 of the Institute of Chemistry of the Romanian Academy. STATISTICA, MobyDigs and SIMCA-P+ acquisition was funded by Ministerul Educatiei, Cercetarii si Tineretului - Autoritatea Nationala pentru Cercetare Stiintifica (MedC-ANCS, contract grant number: 71GR/2006

  8. QSAR STUDY OF FLAVONE / FLAVONOL ANALOGUES AS THE ANTIRADICAL COMPOUNDS BASED ON HANSCH ANALYSIS

    Directory of Open Access Journals (Sweden)

    Iqmal Tahir

    2010-06-01

    Full Text Available Quantitative Structure-Activity Relationship (QSAR analysis of substituted flavone / flavonol compounds has been carried out by applying Hansch Analysis using their physicochemical properties as the predictors. The properties i.e. log P, (log P2, core-core interaction energy (Eint, volume (V, molecular mass (M, dipole moment (μ, heat of formation (ΔHof, binding energy (Ei, total energy (ET, surface area (L, polarizability (α, molar refractivity (RM, hidration energy (EH, electronic energy (Eel and isolated atomic energy (Eat,is, were obtained on the basis of geometry optimization using PM3 semiempirical method. The QSAR analysis used antiradical activities (% A as the dependent variable and has been done by applying multilinear regression technique. The result showed that QSAR equations i.e. % A  =  77.426 - 67.343  [log P] + 3.160 [(log P2 + 67.884 [α] + 6.63x10-4 [ Eint] - 5.280 [L] + 1.179 [V] + 0.447 [M] - 11.000 [μ]  + 0.093 [Ei]  + 3.433 [EH] - 3.44x10-3 [ET] (n = 16 ; r2 = 0.987 ; SD = 9.205; Fcal/Ftable = 4.797   Keywords: QSAR, antiradical, flavone, flavonol

  9. Per- and polyfluoro toxicity (LC(50) inhalation) study in rat and mouse using QSAR modeling.

    Science.gov (United States)

    Bhhatarai, Barun; Gramatica, Paola

    2010-03-15

    Fully or partially fluorinated compounds, known as per- and polyfluorinated chemicals are widely distributed in the environment and released because of their use in different household and industrial products. Few of these long chain per- and polyfluorinated chemicals are classified as emerging pollutants, and their environmental and toxicological effects are unveiled in the literature. This has diverted the production of long chain compounds, considered as more toxic, to short chains, but concerns regarding the toxicity of both types of per- and polyfluorinated chemicals are alarming. There are few experimental data available on the environmental behavior and toxicity of these compounds, and moreover, toxicity profiles are found to be different for the types of animals and species used. Quantitative structure-activity relationship (QSAR) is applied to a combination of short and long chain per- and polyfluorinated chemicals, for the first time, to model and predict the toxicity on two species of rodents, rat (Rattus) and mouse (Mus), by modeling inhalation (LC(50)) data. Multiple linear regression (MLR) models using the ordinary-least-squares (OLS) method, based on theoretical molecular descriptors selected by genetic algorithm (GA), were used for QSAR studies. Training and prediction sets were prepared a priori, and these sets were used to derive statistically robust and predictive (both internally and externally) models. The structural applicability domain (AD) of the model was verified on a larger set of per- and polyfluorinated chemicals retrieved from different databases and journals. The descriptors involved, the similarities, and the differences observed between models pertaining to the toxicity related to the two species are discussed. Chemometric methods such as principal component analysis (PCA) and multidimensional scaling (MDS) were used to select most toxic compounds from those within the AD of both models, which will be subjected to experimental tests

  10. Quantitative structure activity relationship (QSAR) of competitive N-methyl-D-aspartate (NMDA) antagonists

    Science.gov (United States)

    Korkut, Anil; Varnali, Tereza

    Glutamic acid is an excitatory amino acid neurotransmitter in the mammalian central nervous system and the NMDA molecule binds to NMDA-type glutamic acid receptors as a glutamic acid analogue, in vitro. The NMDA-type glutamic acid receptors are known for their function in many neural processes, such as neural plasticity, learning and memory. In addition, excessive NMDA receptor activity has been shown to be related to neurodegenerative diseases like epilepsy so the design of new NMDA antagonists has extra importance as potent drugs for various neural diseases. Potential antagonist molecules are usually synthesized and their activity is measured by experimental techniques. Here, computational chemistry methods are applied to develop a model, which allows one to predict the activity of potent competitive NMDA antagonists. First, various molecular parameters are calculated for a series of competitive NMDA antagonists with known activity values and those parameters are used to make a regression analysis which provides a model that relates the computationally calculated parameters to experimentally determined activity values. By the quantitative structure activity relationship (QSAR) model developed here, it is possible to predict the activity of a potent drug before its synthesis since only theoretically determined molecular parameters are used for the prediction.

  11. 2D-QSAR Study of Indolylpyrimidines Derivative as Antibacterial against Pseudomonas aeruginosa and Staphylococcus aureus: A Comparative Approach

    Directory of Open Access Journals (Sweden)

    Prasanna A. Datar

    2014-01-01

    Full Text Available A set of 15 indolylpyrimidine derivatives with their antibacterial activities in terms of minimum inhibitory concentration against the gram-negative bacteria Pseudomonas aeruginosa and gram-positive Staphylococcus aureus were selected for 2D quantitative structure activity relationship (QSAR analysis. QSAR was performed using a combination of various descriptors such as steric, electronic and topological. Stepwise regression method was used to derive the most significant QSAR equation for predicting the inhibitory activity of this class of molecules. The best QSAR model was further validated by a leave one out technique as well as by the random trials. A high correlation between experimental and predicted inhibitory values was observed. A comparative picture of behavior of indolylpyrimidines against both of the microorganisms is discussed.

  12. QSAR study of selective ligands for the thyroid hormone receptor beta.

    Science.gov (United States)

    Liu, Huanxiang; Gramatica, Paola

    2007-08-01

    In this paper, an accurate and reliable QSAR model of 87 selective ligands for the thyroid hormone receptor beta 1 (TRbeta1) was developed, based on theoretical molecular descriptors to predict the binding affinity of compounds with receptor. The structural characteristics of compounds were described wholly by a large amount of molecular structural descriptors calculated by DRAGON. Six most relevant structural descriptors to the studied activity were selected as the inputs of QSAR model by a robust optimization algorithm Genetic Algorithm. The built model was fully assessed by various validation methods, including internal and external validation, Y-randomization test, chemical applicability domain, and all the validations indicate that the QSAR model we proposed is robust and satisfactory. Thus, the built QSAR model can be used to fast and accurately predict the binding affinity of compounds (in the defined applicability domain) to TRbeta1. At the same time, the model proposed could also identify and provide some insight into what structural features are related to the biological activity of these compounds and provide some instruction for further designing the new selective ligands for TRbeta1 with high activity.

  13. Polar narcosis: Designing a suitable training set for QSAR studies.

    Science.gov (United States)

    Ramos, E U; Vaes, W H; Verhaar, H J; Hermens, J L

    1997-01-01

    Substituted phenols, anilines, pyridines and mononitrobenzenes can be classified as polar narcotics. These chemicals differ from non-polar narcotic compounds not only in their toxic potency (normalized by log K(ow)), but also in their Fish Acute Toxicity Syndrome profiles, together suggesting a different mode of action. For 97 polar narcotics, which are not ionized under physiological conditions, 11 physico-chemical and quantum-chemical descriptors were calculated. Using principal component analysis, 91% of the total variance in this descriptor space could be explained by three principal components which were subsequently used as factors in a statistical design. Eleven compounds were selected based on a two-level full factorial design including three compounds near the center of the chemical domain (a 2(3)+3 design). QSARs were developed for both the design set and the whole set of 63 polar narcotics for which guppy and/or fathead minnow data were available in the literature. Both QSARs, based on partial least squares regression (3 latent variables), resulted in good models (R(2)=0.96 and Q(2)=0.82; R(2)=0.86 and Q(2)=0.83 respectively) and provided similar pseudo-regression coefficients. In addition, the model based on the design chemicals was able to predict the toxicity of the 63 compounds (R(2) =0.85). Models show that acute fish toxicity is determined by hydrophobicity, HOMO-LUMO energy gap and hydrogen-bond acceptor capacity.

  14. Novel 3-Amino-6-chloro-7-(azol-2 or 5-yl-1,1-dioxo-1,4,2-benzodithiazine Derivatives with Anticancer Activity: Synthesis and QSAR Study

    Directory of Open Access Journals (Sweden)

    Aneta Pogorzelska

    2015-12-01

    Full Text Available A series of new 3-amino-6-chloro-7-(azol-2 or 5-yl-1,1-dioxo-1,4,2-benzodithiazine derivatives 5a–j have been synthesized and evaluated in vitro for their antiproliferative activity at the U.S. National Cancer Institute. The most active compound 5h showed significant cytotoxic effects against ovarian (OVCAR-3 and breast (MDA-MB-468 cancer (10% and 47% cancer cell death, respectively as well as a good selectivity toward prostate (DU-145, colon (SW-620 and renal (TK-10 cancer cell lines. To obtain a deeper insight into the structure-activity relationships of the new compounds 5a–j QSAR studies have been applied. Theoretical calculations allowed the identification of molecular descriptors belonging to the RDF (RDF055p and RDF145m in the MOLT-4 and UO-31 QSAR models, respectively and 3D-MorSE (Mor32m and Mor16e for MOLT-4 and UO-31 QSAR models descriptor classes. Based on these data, QSAR models with good robustness and predictive ability have been obtained.

  15. QSAR study of the toxicity of nitrobenzenes to river bacteria and photobacterium phosphoreum

    Energy Technology Data Exchange (ETDEWEB)

    Yuan, X.; Lu, G.; Lang, P. [Northeast Normal Univ., Changchun (China)

    1997-01-01

    Since nitrobenzenes constitute a class of industrial chemicals that are present in Songhua River and probably in many other industrialized countries as well, it is useful to gain insight into their potential hazard to aquatic organisms. For this reason, it was decided to determine data on the toxicity for bacteria in the Songhua River. Furthermore, the toxicity to Ph. phosphoreum was determined in the Microtox assay, in order to further evaluate the usefulness of this assay for hazard assessment. Quantitative structure-activity relationships (QSARs) have been developed for aromatic nitro compound toxicity to aquatic species, but no data on the toxicity of nitrobenzenes to environmental bacteria were used. In this study, the toxicity of various substituted nitrobenzenes to bacteria in Songhua River and to Ph. phosphoreum has been investigated, establishing quantitative structure-activity relationships with n-octanol-water partition coefficient (log P), the energy of the lowest unoccupied molecular orbital (E{sub LUMO}) and the sum of substituent constant ({Sigma}{sigma}-). 12 refs., 2 tabs.

  16. Hepatoprotection of sesquiterpenoids: a quantitative structure-activity relationship (QSAR) approach.

    Science.gov (United States)

    Vinholes, Juliana; Rudnitskaya, Alisa; Gonçalves, Pedro; Martel, Fátima; Coimbra, Manuel A; Rocha, Sílvia M

    2014-03-01

    The relative hepatoprotection effect of fifteen sesquiterpenoids, commonly found in plants and plant-derived foods and beverages was assessed. Endogenous lipid peroxidation (assay A) and induced lipid peroxidation (assay B) were evaluated in liver homogenates from Wistar rats by the thiobarbituric acid reactive species test. Sesquiterpenoids with different chemical structures were tested: trans,trans-farnesol, cis-nerolidol, (-)-α-bisabolol, trans-β-farnesene, germacrene D, α-humulene, β-caryophyllene, isocaryophyllene, (+)-valencene, guaiazulene, (-)-α-cedrene, (+)-aromadendrene, (-)-α-neoclovene, (-)-α-copaene, and (+)-cyclosativene. Ascorbic acid was used as a positive antioxidant control. With the exception of α-humulene, all the sesquiterpenoids under study (1mM) were effective in reducing the malonaldehyde levels in both endogenous and induced lipid peroxidation up to 35% and 70%, respectively. The 3D-QSAR models developed, relating the hepatoprotection activity with molecular properties, showed good fit (Radj(2) 0.819 and 0.972 for the assays A and B, respectively) with good prediction power (Q(2)>0.950 and SDEPstructural and chemical features of sesquiterpenoids such as shape, branching, symmetry, and presence of electronegative fragments, can modulate the hepatoprotective activity observed for these compounds. Copyright © 2013 Elsevier Ltd. All rights reserved.

  17. Triazoloquinazolines as Human A3 Adenosine Receptor Antagonists: A QSAR Study

    Directory of Open Access Journals (Sweden)

    Dae-Sil Lee

    2006-11-01

    Full Text Available Multiple linear regression analysis was performed on the quantitative structure-activity relationships (QSAR of the triazoloquinazoline adenosine antagonists for human A3receptors. The data set used for the QSAR analysis encompassed the activities of 33triazoloquinazoline derivatives and 72 physicochemical descriptors. A template moleculewas derived using the known molecular structure for one of the compounds when bound tothe human A2B receptor, in which the amide bond was in a cis-conformation. All the testcompounds were aligned to the template molecule. In order to identify a reasonable QSARequation to describe the data set, we developed a multiple linear regression program thatexamined every possible combination of descriptors. The QSAR equation derived from thisanalysis indicates that the spatial and electronic effects is greater than that of hydrophobiceffects in binding of the antagonists to the human A3 receptor. It also predicts that a largesterimol length parameter is advantageous to activity, whereas large sterimol widthparameters and fractional positive partial surface areas are nonadvatageous.

  18. A QSAR Study of Environmental Estrogens Based on a Novel Variable Selection Method

    Directory of Open Access Journals (Sweden)

    Aiqian Zhang

    2012-05-01

    Full Text Available A large number of descriptors were employed to characterize the molecular structure of 53 natural, synthetic, and environmental chemicals which are suspected of disrupting endocrine functions by mimicking or antagonizing natural hormones and may thus pose a serious threat to the health of humans and wildlife. In this work, a robust quantitative structure-activity relationship (QSAR model with a novel variable selection method has been proposed for the effective estrogens. The variable selection method is based on variable interaction (VSMVI with leave-multiple-out cross validation (LMOCV to select the best subset. During variable selection, model construction and assessment, the Organization for Economic Co-operation and Development (OECD principles for regulation of QSAR acceptability were fully considered, such as using an unambiguous multiple-linear regression (MLR algorithm to build the model, using several validation methods to assessment the performance of the model, giving the define of applicability domain and analyzing the outliers with the results of molecular docking. The performance of the QSAR model indicates that the VSMVI is an effective, feasible and practical tool for rapid screening of the best subset from large molecular descriptors.

  19. Synthesis, anticancer activity and QSAR study of 1,4-naphthoquinone derivatives.

    Science.gov (United States)

    Prachayasittikul, Veda; Pingaew, Ratchanok; Worachartcheewan, Apilak; Nantasenamat, Chanin; Prachayasittikul, Supaluk; Ruchirawat, Somsak; Prachayasittikul, Virapong

    2014-09-12

    A series of 2-substituted amino-3-chloro-1,4-naphthoquinone derivatives (3-12) were synthesized as anticancer agents and tested against four cancer cell lines including HepG2, HuCCA-1, A549 and MOLT-3. The most potent cytotoxic activity against the HepG2, HuCCA-1 and A549 cell lines was found to be m-acetylphenylamino-1,4-naphthoquinone (8) affording IC50 values of 4.758, 2.364 and 12.279 μM, respectively. On the other hand, p-acetylphenylamino-1,4-naphthoquinone (9) exhibited the most potent cytotoxic activity against the MOLT-3 cell line with an IC50 of 2.118 μM. Quantitative structure-activity relationship (QSAR) investigations provided good predictive performance as observed from cross-validated R of 0.9177-0.9753 and RMSE of 0.0614-0.1881. The effects of substituents at the 2-amino position on the naphthoquinone core structure and its corresponding influence on the cytotoxic activity were investigated by virtually constructing additional 1,4-naphthoquinone compounds (13-36) for which cytotoxic activities were predicted using equations obtained from the previously constructed QSAR models. Interpretation of informative descriptors from QSAR models revealed pertinent knowledge on physicochemical properties governing the cytotoxic activities of tested cancer cell lines. It is anticipated that the QSAR models developed herein could provide guidelines for further development of novel and potent anticancer agents. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

  20. Docking Based 3D-QSAR Study of Tricyclic Guanidine Analogues of Batzelladine K As Anti-Malarial Agents

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    Nafees Ahmed

    2017-06-01

    Full Text Available The Plasmodium falciparum Lactate Dehydrogenase enzyme (PfLDH catalyzes inter-conversion of pyruvate to lactate during glycolysis producing the energy required for parasitic growth. The PfLDH has been studied as a potential molecular target for development of anti-malarial agents. In an attempt to find the potent inhibitor of PfLDH, we have used Discovery studio to perform molecular docking in the active binding pocket of PfLDH by CDOCKER, followed by three-dimensional quantitative structure-activity relationship (3D-QSAR studies of tricyclic guanidine batzelladine compounds, which were previously synthesized in our laboratory. Docking studies showed that there is a very strong correlation between in silico and in vitro results. Based on docking results, a highly predictive 3D-QSAR model was developed with q2 of 0.516. The model has predicted r2 of 0.91 showing that predicted IC50 values are in good agreement with experimental IC50 values. The results obtained from this study revealed the developed model can be used to design new anti-malarial compounds based on tricyclic guanidine derivatives and to predict activities of new inhibitors.

  1. Docking Based 3D-QSAR Study of Tricyclic Guanidine Analogues of Batzelladine K as anti-malarial agents

    Science.gov (United States)

    Ahmed, Nafees; Anwar, Sirajudheen; Thet Htar, Thet

    2017-06-01

    The Plasmodium falciparum Lactate Dehydrogenase enzyme (PfLDH) catalyzes inter-conversion of pyruvate to lactate during glycolysis producing the energy required for parasitic growth. The PfLDH has been studied as a potential molecular target for development of anti-malarial agents. In an attempt to find the potent inhibitor of PfLDH, we have used Discovery studio to perform molecular docking in the active binding pocket of PfLDH by CDOCKER, followed by three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of tricyclic guanidine batzelladine compounds, which were previously synthesized in our laboratory. Docking studies showed that there is a very strong correlation between in silico and in vitro results. Based on docking results, a highly predictive 3D-QSAR model was developed with q2 of 0.516. The model has predicted r2 of 0.91 showing that predicted IC50 values are in good agreement with experimental IC50 values. The results obtained from this study revealed the developed model can be used to design new anti-malarial compounds based on tricyclic guanidine derivatives and to predict activities of new inhibitors.

  2. Synthesis, algal inhibition activities and QSAR studies of novel gramine compounds containing ester functional groups

    Science.gov (United States)

    Li, Xia; Yu, Liangmin; Jiang, Xiaohui; Xia, Shuwei; Zhao, Haizhou

    2009-05-01

    2,5,6-Tribromo-1-methylgramine (TBG), isolated from bryozoan Zoobotryon pellucidum was shown to be very efficient in preventing recruitment of larval settlement. In order to improve the compatibility of TBG and its analogues with other ingredients in antifouling paints, structural modification of TBG was focused mainly on halogen substitution and N-substitution. Two halogen-substitute gramines and their derivatives which contain ester functional groups at N-position of gramines were synthesized. Algal inhibition activities of the synthesized compounds against algae Nitzschia closterium were evaluated and the Median Effective Concentration (EC50) range was 1.06-6.74 μg ml-1. Compounds that had a long chain ester group exhibited extremely high antifouling activity. Quantitive Structure Activity Relationship (QSAR) studies with multiple linear regression analysis were applied to find correlation between different calculated molecular descriptors and biological activity of the synthesized compounds. The results show that the toxicity (log (1/EC50)) is correlated well with the partition coefficient log P. Thus, these products have potential function as antifouling agents.

  3. QSAR Study of Insecticides of Phthalamide Derivatives Using Multiple Linear Regression and Artificial Neural Network Methods

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    Adi Syahputra

    2014-03-01

    Full Text Available Quantitative structure activity relationship (QSAR for 21 insecticides of phthalamides containing hydrazone (PCH was studied using multiple linear regression (MLR, principle component regression (PCR and artificial neural network (ANN. Five descriptors were included in the model for MLR and ANN analysis, and five latent variables obtained from principle component analysis (PCA were used in PCR analysis. Calculation of descriptors was performed using semi-empirical PM6 method. ANN analysis was found to be superior statistical technique compared to the other methods and gave a good correlation between descriptors and activity (r2 = 0.84. Based on the obtained model, we have successfully designed some new insecticides with higher predicted activity than those of previously synthesized compounds, e.g.2-(decalinecarbamoyl-5-chloro-N’-((5-methylthiophen-2-ylmethylene benzohydrazide, 2-(decalinecarbamoyl-5-chloro-N’-((thiophen-2-yl-methylene benzohydrazide and 2-(decaline carbamoyl-N’-(4-fluorobenzylidene-5-chlorobenzohydrazide with predicted log LC50 of 1.640, 1.672, and 1.769 respectively.

  4. QSAR study of benzimidazole derivatives inhibition on escherichia coli methionine Aminopeptidase

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    Zahra Garkani-Nejad

    2010-06-01

    Full Text Available The paper describes a quantitative structure-activity relationship (QSAR study of IC50 values of benzimidazole derivatives on escherichia coli methionine aminopeptidase. The activity of the 32 inhibitors has been estimated by means of multiple linear regression (MLR and artificial neural network (ANN techniques. The results obtained using the MLR method indicate that the activity of derivatives of benzimidazoles on CoII-loaded escherichia coli methionine aminopeptidase depend on different parameters containing topological descriptors, Burden eigen values, 3D MoRSE descriptors and 2D autocorrelation descriptors. The best artificial neural network model is a fully-connected, feed forward back propagation network with a 5-4-1 architecture. Standard error for the training set using this network was 0.193 with correlation coefficient 0.996 and for the prediction set standard error was 1.41 with correlation coefficient 0.802. Comparison of the quality of the ANN with different MLR models showed that ANN has a better predictive power.

  5. DFT Based QSAR Study of Enzyme Ribonucleoside Diphosphate Reductase

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    Mohiuddin Ansari

    2010-01-01

    Full Text Available Quantum chemical descriptors such as heat of formation, energy of HOMO, total energy, absolute hardness and chemical potential in different combinations have been used to develop QSAR models of inhibitors of enzyme ribonucleoside diphosphate reductase, RDR. The inhibitors are mainly derivatives of 1-formylisoquinoline thiosemicarbazone and 2-formylpyridine thiosemicarbazone. The values of various descriptors have been evaluated with the help of Win MOPAC 7.21 software using DFT method. Multiple linear regression analysis has been made with the help of above mentioned descriptors using the same software. Regression equations have been found to be successful models as indicated by the regression coefficient r2 having the value as high as 0.96 and cross validation coefficient rCV2 having the value approaching 0.95. The value of these two coefficients is indicative of high order of reliability for the proposed prediction. The results obtained are also validated on account of the closeness of observed and predicted inhibitory activities. The best combination of descriptors is heat of formation, total energy and energy of HOMO. Thus the prediction of suitability of inhibitors of the enzyme RDR can be made with the help of the best regression equation.

  6. (Q)SAR tools for priority setting: A case study with printed paper and board food contact material substances.

    Science.gov (United States)

    Van Bossuyt, Melissa; Van Hoeck, Els; Raitano, Giuseppa; Manganelli, Serena; Braeken, Els; Ates, Gamze; Vanhaecke, Tamara; Van Miert, Sabine; Benfenati, Emilio; Mertens, Birgit; Rogiers, Vera

    2017-04-01

    Over the last years, more stringent safety requirements for an increasing number of chemicals across many regulatory fields (e.g. industrial chemicals, pharmaceuticals, food, cosmetics, …) have triggered the need for an efficient screening strategy to prioritize the substances of highest concern. In this context, alternative methods such as in silico (i.e. computational) techniques gain more and more importance. In the current study, a new prioritization strategy for identifying potentially mutagenic substances was developed based on the combination of multiple (quantitative) structure-activity relationship ((Q)SAR) tools. Non-evaluated substances used in printed paper and board food contact materials (FCM) were selected for a case study. By applying our strategy, 106 out of the 1723 substances were assigned 'high priority' as they were predicted mutagenic by 4 different (Q)SAR models. Information provided within the models allowed to identify 53 substances for which Ames mutagenicity prediction already has in vitro Ames test results. For further prioritization, additional support could be obtained by applying local i.e. specific models, as demonstrated here for aromatic azo compounds, typically found in printed paper and board FCM. The strategy developed here can easily be applied to other groups of chemicals facing the same need for priority ranking. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. AutoGPA-Based 3D-QSAR Modeling and Molecular Docking Study on Factor Xa Inhibitors as Anticoagulant Agents

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    Guo Fang Yuan

    2016-01-01

    Full Text Available The three-dimensional-quantitative structure activity relationship (3D-QSAR studies were performed on a series of direct factor Xa (FXa inhibitors using AutoGPA-based modeling method in this paper. A training set of 38 molecules and a test set containing 10 molecules were used to build the 3D-QSAR model and validate the derived model, respectively. The developed model with correlation coefficients (r2 of 0.8564 and cross-validated correlation coefficients (q2 of 0.6721 were validated by an external test set of 10 molecules with predicted correlation coefficient (rpred2 of 0.6077. Docking study of FXa inhibitors and FXa active site was performed to check the induced pharmacophore query and comparative molecular field analysis (CoMFA contour maps using MOE2012.10. It was proved to be coincidence with the interaction information between ligand and FXa active site and was rendered to provide a useful tool to improve FXa inhibitors.

  8. A QSAR Study of Some Cyclobutenediones as CCR1 Antagonists by Artificial Neural Networks Based on Principal Component Analysis

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    E Arkan

    2011-12-01

    Full Text Available Background and the purpose of the study: A quantitative structure activity relationship (QSAR model based on artificial neural networks (ANN was developed to study the activities of 29 derivatives of 3-amino-4-(2-(2-(4-benzylpiperazin-1-yl-2-oxoethoxy phenylamino cyclobutenedione as C-C chemokine receptor type 1(CCR1 inhibitors. Methods: A feed-forward ANN with error back-propagation learning algorithm was used for model building which was achieved by optimizing initial learning rate, learning momentum, epoch and the number of hidden neurons. Results: Good results were obtained with a Root Mean Square Error (RMSE and correlation coefficients (R2 of 0.189 and 0.906 for the training and 0.103 and 0.932 prediction sets, respectively. Conclusion: The results reflect a nonlinear relationship between the Principal components obtained from calculated molecular descriptors and the inhibitory activities of the investigated molecules.

  9. Molecular docking, 3D QSAR and dynamics simulation studies of imidazo-pyrrolopyridines as janus kinase 1 (JAK 1) inhibitors.

    Science.gov (United States)

    Itteboina, Ramesh; Ballu, Srilata; Sivan, Sree Kanth; Manga, Vijjulatha

    2016-10-01

    Janus kinase 1 (JAK 1) plays a critical role in initiating responses to cytokines by the JAK-signal transducer and activator of transcription (JAK-STAT). This controls survival, proliferation and differentiation of a variety of cells. Docking, 3D quantitative structure activity relationship (3D-QSAR) and molecular dynamics (MD) studies were performed on a series of Imidazo-pyrrolopyridine derivatives reported as JAK 1 inhibitors. QSAR model was generated using 30 molecules in the training set; developed model showed good statistical reliability, which is evident from r2ncv and r2loo values. The predictive ability of this model was determined using a test set of 13 molecules that gave acceptable predictive correlation (r2Pred) values. Finally, molecular dynamics simulation was performed to validate docking results and MM/GBSA calculations. This facilitated us to compare binding free energies of cocrystal ligand and newly designed molecule R1. The good concordance between the docking results and CoMFA/CoMSIA contour maps afforded obliging clues for the rational modification of molecules to design more potent JAK 1 inhibitors. Copyright © 2016 Elsevier Ltd. All rights reserved.

  10. A QSAR study of some cyclobutenediones as CCR1 antagonists by artificial neural networks based on principal component analysis.

    Science.gov (United States)

    Shahlaei, M; Fassihi, A; Saghaie, L; Arkan, E; Pourhossein, A

    2011-01-01

    A quantitative structure activity relationship (QSAR) model based on artificial neural networks (ANN) was developed to study the activities of 29 derivatives of 3-amino-4-(2-(2-(4-benzylpiperazin-1-yl)-2-oxoethoxy) phenylamino) cyclobutenedione as C-C chemokine receptor type 1(CCR1) inhibitors. A feed-forward ANN with error back-propagation learning algorithm was used for model building which was achieved by optimizing initial learning rate, learning momentum, epoch and the number of hidden neurons. Good results were obtained with a Root Mean Square Error (RMSE) and correlation coefficients (R(2)) of 0.189 and 0.906 for the training and 0.103 and 0.932 prediction sets, respectively. The results reflect a nonlinear relationship between the Principal components obtained from calculated molecular descriptors and the inhibitory activities of the investigated molecules.

  11. Support vector machine based training of multilayer feedforward neural networks as optimized by particle swarm algorithm: application in QSAR studies of bioactivity of organic compounds.

    Science.gov (United States)

    Lin, Wei-Qi; Jiang, Jian-Hui; Zhou, Yan-Ping; Wu, Hai-Long; Shen, Guo-Li; Yu, Ru-Qin

    2007-01-30

    Multilayer feedforward neural networks (MLFNNs) are important modeling techniques widely used in QSAR studies for their ability to represent nonlinear relationships between descriptors and activity. However, the problems of overfitting and premature convergence to local optima still pose great challenges in the practice of MLFNNs. To circumvent these problems, a support vector machine (SVM) based training algorithm for MLFNNs has been developed with the incorporation of particle swarm optimization (PSO). The introduction of the SVM based training mechanism imparts the developed algorithm with inherent capacity for combating the overfitting problem. Moreover, with the implementation of PSO for searching the optimal network weights, the SVM based learning algorithm shows relatively high efficiency in converging to the optima. The proposed algorithm has been evaluated using the Hansch data set. Application to QSAR studies of the activity of COX-2 inhibitors is also demonstrated. The results reveal that this technique provides superior performance to backpropagation (BP) and PSO training neural networks.

  12. 2D-QSAR and 3D-QSAR/CoMSIA Studies on a Series of (R-2-((2-(1H-Indol-2-ylethylamino-1-Phenylethan-1-ol with Human β3-Adrenergic Activity

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    Gastón Apablaza

    2017-03-01

    Full Text Available The β3 adrenergic receptor is raising as an important drug target for the treatment of pathologies such as diabetes, obesity, depression, and cardiac diseases among others. Several attempts to obtain selective and high affinity ligands have been made. Currently, Mirabegron is the only available drug on the market that targets this receptor approved for the treatment of overactive bladder. However, the FDA (Food and Drug Administration in USA and the MHRA (Medicines and Healthcare products Regulatory Agency in UK have made reports of potentially life-threatening side effects associated with the administration of Mirabegron, casting doubts on the continuity of this compound. Therefore, it is of utmost importance to gather information for the rational design and synthesis of new β3 adrenergic ligands. Herein, we present the first combined 2D-QSAR (two-dimensional Quantitative Structure-Activity Relationship and 3D-QSAR/CoMSIA (three-dimensional Quantitative Structure-Activity Relationship/Comparative Molecular Similarity Index Analysis study on a series of potent β3 adrenergic agonists of indole-alkylamine structure. We found a series of changes that can be made in the steric, hydrogen-bond donor and acceptor, lipophilicity and molar refractivity properties of the compounds to generate new promising molecules. Finally, based on our analysis, a summary and a regiospecific description of the requirements for improving β3 adrenergic activity is given.

  13. QSAR study of estrogens with the help of PM3-based descriptors

    Science.gov (United States)

    Pasha, F. A.; Srivastava, H. K.; Singh, P. P.

    Quantum chemical descriptors (γHOMO, γLUMO, absolute hardness, global softness, chemical potential, and electronegativity) and energy descriptors (Qmin, ΔH 0f, ET, and EE) based QSAR study of estrogen derivatives was made with the help of PM3 calculations on WinMOPAC 7.21 software. The observed RBA values of estrogens were taken from the literature. QSAR models were made using different quantum chemical and energy descriptors with the help of multiple linear regression analysis. Regression models indicate that absolute hardness in combination with different energy descriptors provide better correlation between observed relative binding affinity (RBA) and predicted relative binding affinity (PA). Regression models for other quantum chemical descriptors with energy descriptors are not as clear as in the case of absolute hardness. Hardness provides a better picture due to the maximum hardness principle and can be used as a QSAR model for predicting the biological activity of any compound. Content:text/plain; charset="UTF-8"

  14. 2D-QSAR and 3D-QSAR Analyses for EGFR Inhibitors

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    Manman Zhao

    2017-01-01

    Full Text Available Epidermal growth factor receptor (EGFR is an important target for cancer therapy. In this study, EGFR inhibitors were investigated to build a two-dimensional quantitative structure-activity relationship (2D-QSAR model and a three-dimensional quantitative structure-activity relationship (3D-QSAR model. In the 2D-QSAR model, the support vector machine (SVM classifier combined with the feature selection method was applied to predict whether a compound was an EGFR inhibitor. As a result, the prediction accuracy of the 2D-QSAR model was 98.99% by using tenfold cross-validation test and 97.67% by using independent set test. Then, in the 3D-QSAR model, the model with q2=0.565 (cross-validated correlation coefficient and r2=0.888 (non-cross-validated correlation coefficient was built to predict the activity of EGFR inhibitors. The mean absolute error (MAE of the training set and test set was 0.308 log units and 0.526 log units, respectively. In addition, molecular docking was also employed to investigate the interaction between EGFR inhibitors and EGFR.

  15. QSAR study on the histamine (H3 receptor antagonists using the genetic algorithm: Multi parameter linear regression

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    Adimi Maryam

    2012-01-01

    Full Text Available A quantitative structure activity relationship (QSAR model has been produced for predicting antagonist potency of biphenyl derivatives as human histamine (H3 receptors. The molecular structures of the compounds are numerically represented by various kinds of molecular descriptors. The whole data set was divided into training and test sets. Genetic algorithm based multiple linear regression is used to select most statistically effective descriptors. The final QSAR model (N =24, R2=0.916, F = 51.771, Q2 LOO = 0.872, Q2 LGO = 0.847, Q2 BOOT = 0.857 was fully validated employing leaveone- out (LOO cross-validation approach, Fischer statistics (F, Yrandomisation test, and predictions based on the test data set. The test set presented an external prediction power of R2 test=0.855. In conclusion, the QSAR model generated can be used as a valuable tool for designing similar groups of new antagonists of histamine (H3 receptors.

  16. Quantitative Structure--Activity Relationship Modeling of Rat Acute Toxicity by Oral Exposure

    Science.gov (United States)

    Background: Few Quantitative Structure-Activity Relationship (QSAR) studies have successfully modeled large, diverse rodent toxicity endpoints. Objective: In this study, a combinatorial QSAR approach has been employed for the creation of robust and predictive models of acute toxi...

  17. Current Mathematical Methods Used in QSAR/QSPR Studies

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    Peixun Liu

    2009-04-01

    Full Text Available This paper gives an overview of the mathematical methods currently used in quantitative structure-activity/property relationship (QASR/QSPR studies. Recently, the mathematical methods applied to the regression of QASR/QSPR models are developing very fast, and new methods, such as Gene Expression Programming (GEP, Project Pursuit Regression (PPR and Local Lazy Regression (LLR have appeared on the QASR/QSPR stage. At the same time, the earlier methods, including Multiple Linear Regression (MLR, Partial Least Squares (PLS, Neural Networks (NN, Support Vector Machine (SVM and so on, are being upgraded to improve their performance in QASR/QSPR studies. These new and upgraded methods and algorithms are described in detail, and their advantages and disadvantages are evaluated and discussed, to show their application potential in QASR/QSPR studies in the future.

  18. The interplay between QSAR/QSPR studies and partial order ranking and formal concept analyses.

    Science.gov (United States)

    Carlsen, Lars

    2009-04-17

    The often observed scarcity of physical-chemical and well as toxicological data hampers the assessment of potentially hazardous chemicals released to the environment. In such cases Quantitative Structure-Activity Relationships/Quantitative Structure-Property Relationships (QSAR/QSPR) constitute an obvious alternative for rapidly, effectively and inexpensively generatng missing experimental values. However, typically further treatment of the data appears necessary, e.g., to elucidate the possible relations between the single compounds as well as implications and associations between the various parameters used for the combined characterization of the compounds under investigation. In the present paper the application of QSAR/QSPR in combination with Partial Order Ranking (POR) methodologies will be reviewed and new aspects using Formal Concept Analysis (FCA) will be introduced. Where POR constitutes an attractive method for, e.g., prioritizing a series of chemical substances based on a simultaneous inclusion of a range of parameters, FCA gives important information on the implications associations between the parameters. The combined approach thus constitutes an attractive method to a preliminary assessment of the impact on environmental and human health by primary pollutants or possibly by a primary pollutant well as a possible suite of transformation subsequent products that may be both persistent in and bioaccumulating and toxic. The present review focus on the environmental - and human health impact by residuals of the rocket fuel 1,1-dimethylhydrazine (heptyl) and its transformation products as an illustrative example.

  19. Synthesis, antifungal activity, and QSAR studies of 1,6-dihydropyrimidine derivatives

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    Chirag Rami

    2013-01-01

    Full Text Available Introduction: A practical synthesis of pyrimidinone would be very helpful for chemists because pyrimidinone is found in many bioactive natural products and exhibits a wide range of biological properties. The biological significance of pyrimidine derivatives has led us to the synthesis of substituted pyrimidine. Materials and Methods: With the aim of developing potential antimicrobials, new series of 5-cyano-6-oxo-1,6-dihydro-pyrimidine derivatives namely 2-(5-cyano-6-oxo-4-substituted (aryl-1,6-dihydropyrimidin-2-ylthio-N-substituted (phenyl acetamide (C1-C41 were synthesized and characterized by Fourier transform infrared spectroscopy (FTIR, mass analysis, and proton nuclear magnetic resonance ( 1 H NMR. All the compounds were screened for their antifungal activity against Candida albicans (MTCC, 227. Results and Discussion: Quantitative structure activity relationship (QSAR studies of a series of 1,6-dihydro-pyrimidine were carried out to study various structural requirements for fungal inhibition. Various lipophilic, electronic, geometric, and spatial descriptors were correlated with antifungal activity using genetic function approximation. Developed models were found predictive as indicated by their square of predictive regression values (r 2pred and their internal and external cross-validation. Study reveals that CHI_3_C, Molecular_SurfaceArea, and Jurs_DPSA_1 contributed significantly to the activity along with some electronic, geometric, and quantum mechanical descriptors. Conclusion: A careful analysis of the antifungal activity data of synthesized compounds revealed that electron withdrawing substitution on N-phenyl acetamide ring of 1,6-dihydropyrimidine moiety possess good activity.

  20. QSAR and docking studies of coumarin derivatives as potent HIV-1 integrase inhibitors

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    V.K. Srivastav

    2017-02-01

    Full Text Available Human immunodeficiency virus integrase (HIV-1IN is an emerging and potential drug target for anti-HIV therapy. It is an enzyme essential for 3′ processing and integration step in the life cycle of HIV. In the present study a series of coumarin derivatives (containing 26 compounds as HIV-1IN inhibitors was subjected to quantitative structure–activity relationship (QSAR analysis. For building the regression models two different variable selection approaches namely, genetic function approximation (GFA and sequential multiple linear regression (SQ-MLR were used and compared to predict the HIV-1IN inhibition activity. Based on prediction, the best validation model for 3′ processing inhibition activity with squared correlation coefficient (r2 = 0.8965, cross validated correlation coefficient (Q2 = 0.8307 and external prediction ability pred_r2 = 0.5400 showed that Henry’s law Constant (HLC, Partition Coefficient (PC and Dipole moment-Z component (D3 were the positive contributors, whereas for integration inhibition activity, parameters r2 = 0.8904, Q2 = 0.8174 and pred_r2 = 0.7159 showed HLC, Logarithm of Partition Coefficient (LogP and Dipole moment-Y component (D2 contributed positively to the activity. The binding mode pattern of the compounds to the binding site of integrase enzyme was confirmed by docking studies. The results of the present study may be useful for designing more potent HIV-1IN inhibitors.

  1. In Vitro Antioxidant Activity of Selected 4-Hydroxy-chromene-2-one Derivatives—SAR, QSAR and DFT Studies

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    Slavica Solujić

    2011-04-01

    Full Text Available The series of fifteen synthesized 4-hydroxycoumarin derivatives was subjected to antioxidant activity evaluation in vitro, through total antioxidant capacity, 1,1-diphenyl-2-picryl-hydrazyl (DPPH, hydroxyl radical, lipid peroxide scavenging and chelating activity. The highest activity was detected during the radicals scavenging, with 2b, 6b, 2c, and 4c noticed as the most active. The antioxidant activity was further quantified by the quantitative structure-activity relationships (QSAR studies. For this purpose, the structures were optimized using Paramethric Method 6 (PM6 semi-empirical and Density Functional Theory (DFT B3LYP methods. Bond dissociation enthalpies of coumarin 4-OH, Natural Bond Orbital (NBO gained hybridization of the oxygen, acidity of the hydrogen atom and various molecular descriptors obtained, were correlated with biological activity, after which we designed 20 new antioxidant structures, using the most favorable structural motifs, with much improved predicted activity in vitro.

  2. 3D-QSAR studies on a series of dihydroorotate dehydrogenase inhibitors: analogues of the active metabolite of leflunomide.

    Science.gov (United States)

    Li, Shun-Lai; He, Mao-Yu; Du, Hong-Guang

    2011-01-01

    The active metabolite of the novel immunosuppressive agent leflunomide has been shown to inhibit the enzyme dihydroorotate dehydrogenase (DHODH). This enzyme catalyzes the fourth step in de novo pyrimidine biosynthesis. Self-organizing molecular field analysis (SOMFA), a simple three-dimensional quantitative structure-activity relationship (3D-QSAR) method is used to study the correlation between the molecular properties and the biological activities of a series of analogues of the active metabolite. The statistical results, cross-validated r(CV) (2) (0.664) and non cross-validated r(2) (0.687), show a good predictive ability. The final SOMFA model provides a better understanding of DHODH inhibitor-enzyme interactions, and may be useful for further modification and improvement of inhibitors of this important enzyme.

  3. 3D-QSAR Studies on a Series of Dihydroorotate Dehydrogenase Inhibitors: Analogues of the Active Metabolite of Leflunomide

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    Hong-Guang Du

    2011-05-01

    Full Text Available The active metabolite of the novel immunosuppressive agent leflunomide has been shown to inhibit the enzyme dihydroorotate dehydrogenase (DHODH. This enzyme catalyzes the fourth step in de novo pyrimidine biosynthesis. Self-organizing molecular field analysis (SOMFA, a simple three-dimensional quantitative structure-activity relationship (3D-QSAR method is used to study the correlation between the molecular properties and the biological activities of a series of analogues of the active metabolite. The statistical results, cross-validated rCV2 (0.664 and non cross-validated r2 (0.687, show a good predictive ability. The final SOMFA model provides a better understanding of DHODH inhibitor-enzyme interactions, and may be useful for further modification and improvement of inhibitors of this important enzyme.

  4. A new strategy to improve the predictive ability of the local lazy regression and its application to the QSAR study of melanin-concentrating hormone receptor 1 antagonists.

    Science.gov (United States)

    Li, Jiazhong; Li, Shuyan; Lei, Beilei; Liu, Huanxiang; Yao, Xiaojun; Liu, Mancang; Gramatica, Paola

    2010-04-15

    In the quantitative structure-activity relationship (QSAR) study, local lazy regression (LLR) can predict the activity of a query molecule by using the information of its local neighborhood without need to produce QSAR models a priori. When a prediction is required for a query compound, a set of local models including different number of nearest neighbors are identified. The leave-one-out cross-validation (LOO-CV) procedure is usually used to assess the prediction ability of each model, and the model giving the lowest LOO-CV error or highest LOO-CV correlation coefficient is chosen as the best model. However, it has been proved that the good statistical value from LOO cross-validation appears to be the necessary, but not the sufficient condition for the model to have a high predictive power. In this work, a new strategy is proposed to improve the predictive ability of LLR models and to access the accuracy of a query prediction. The bandwidth of k neighbor value for LLR is optimized by considering the predictive ability of local models using an external validation set. This approach was applied to the QSAR study of a series of thienopyrimidinone antagonists of melanin-concentrating hormone receptor 1. The obtained results from the new strategy shows evident improvement compared with the commonly used LOO-CV LLR methods and the traditional global linear model. 2009 Wiley Periodicals, Inc.

  5. An integrated QSAR modeling approach to explore the structure-property and selectivity relationships of N-benzoyl-L-biphenylalanines as integrin antagonists.

    Science.gov (United States)

    Amin, Sk Abdul; Adhikari, Nilanjan; Bhargava, Sonam; Gayen, Shovanlal; Jha, Tarun

    2017-11-17

    Integrins [Formula: see text] and [Formula: see text] are important targets to treat different inflammatory diseases, such as multiple sclerosis, inflammatory bowel diseases, rheumatoid arthritis, atherosclerosis, and asthma. Despite being valuable targets, only a few work has been reported to date regarding molecular modeling studies on these integrins. Not only that, none of these reports addressed the selectivity issue between integrins [Formula: see text] and [Formula: see text]. Therefore, a major challenge regarding the design and discovery of selective integrin antagonists remains. In this study, a series of 142 N-benzoyl-L-biphenylalanines having both integrin [Formula: see text] and [Formula: see text] inhibitory activities were considered for a variety of QSAR approaches including regression and classification-based 2D-QSARs, Hologram QSARs, 3D-QSAR CoMFA and CoMSIA studies to identify the structural requirements of these integrin antagonists. All these QSAR models were statistically validated and subsequently correlated with each other to get a detailed understanding of the activity and selectivity profiles of these molecules.

  6. 3D-QSAR/CoMFA-Based Structure-Affinity/Selectivity Relationships of Aminoalkylindoles in the Cannabinoid CB1 and CB2 Receptors

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    Jaime Mella-Raipán

    2014-03-01

    Full Text Available A 3D-QSAR (CoMFA study was performed in an extensive series of aminoalkylindoles derivatives with affinity for the cannabinoid receptors CB1 and CB2. The aim of the present work was to obtain structure-activity relationships of the aminoalkylindole family in order to explain the affinity and selectivity of the molecules for these receptors. Major differences in both, steric and electrostatic fields were found in the CB1 and CB2 CoMFA models. The steric field accounts for the principal contribution to biological activity. These results provide a foundation for the future development of new heterocyclic compounds with high affinity and selectivity for the cannabinoid receptors with applications in several pathological conditions such as pain treatment, cancer, obesity and immune disorders, among others.

  7. 3D-QSAR/CoMFA-based structure-affinity/selectivity relationships of aminoalkylindoles in the cannabinoid CB1 and CB2 receptors.

    Science.gov (United States)

    Mella-Raipán, Jaime; Hernández-Pino, Santiago; Morales-Verdejo, César; Pessoa-Mahana, David

    2014-03-05

    A 3D-QSAR (CoMFA) study was performed in an extensive series of aminoalkylindoles derivatives with affinity for the cannabinoid receptors CB1 and CB2. The aim of the present work was to obtain structure-activity relationships of the aminoalkylindole family in order to explain the affinity and selectivity of the molecules for these receptors. Major differences in both, steric and electrostatic fields were found in the CB1 and CB2 CoMFA models. The steric field accounts for the principal contribution to biological activity. These results provide a foundation for the future development of new heterocyclic compounds with high affinity and selectivity for the cannabinoid receptors with applications in several pathological conditions such as pain treatment, cancer, obesity and immune disorders, among others.

  8. Synthesis and quantitative structure-activity relationship (QSAR) analysis of some novel oxadiazolo[3,4-d]pyrimidine nucleosides derivatives as antiviral agents.

    Science.gov (United States)

    Xu, Xiaojuan; Wang, Jun; Yao, Qizheng

    2015-01-15

    We have synthesized a series of 4H,6H-[1,2,5]oxadiazolo[3,4-d]pyrimidine-5,7-dione 1-oxide nucleoside and their anti-vesicular stomatitis virus (VSV) activities in Wish cell were also investigated in vitro. It was found that most compounds showed obvious anti-VSV activities and compound 9 with ribofuranoside improved the anti-VSV activity by approximately 10 times and 18 times compared to didanosine (DDI) and acyclovir, respectively. A quantitative structure-activity relationship (QSAR) study of these compounds as well as previous reported oxadiazolo[3,4-d]pyrimidine nucleoside derivatives indicated that compounds with high activity should have small values of logP(o/w), vsurf_G and a large logS value. These findings and results provide a base for further investigations. Copyright © 2014 Elsevier Ltd. All rights reserved.

  9. Relationship between soybean yield/quality and soil quality in a major soybean-producing area based on a 2D-QSAR model

    Science.gov (United States)

    Gao, Ming; Li, Shiwei

    2017-05-01

    Based on experimental data of the soybean yield and quality from 30 sampling points, a quantitative structure-activity relationship model (2D-QSAR) was established using the soil quality (elements, pH, organic matter content and cation exchange capacity) as independent variables and soybean yield or quality as the dependent variable, with SPSS software. During the modeling, the full data set (30 and 14 compounds) was divided into a training set (24 and 11 compounds) for model generation and a test set (6 and 3 compounds) for model validation. The R2 values of the resulting models and data were 0.826 and 0.808 for soybean yield and quality, respectively, and all regression coefficients were significant (P study other crops or regions, providing a theoretical basis to improving the yield and quality of crops.

  10. Application of 4D-QSAR Studies to a Series of Raloxifene Analogs and Design of Potential Selective Estrogen Receptor Modulators

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    Carlos Rangel Rodrigues

    2012-06-01

    Full Text Available Four-dimensional quantitative structure-activity relationship (4D-QSAR analysis was applied on a series of 54 2-arylbenzothiophene derivatives, synthesized by Grese and coworkers, based on raloxifene (an estrogen receptor-alpha antagonist, and evaluated as ERa ligands and as inhibitors of estrogen-stimulated proliferation of MCF-7 breast cancer cells. The conformations of each analogue, sampled from a molecular dynamics simulation, were placed in a grid cell lattice according to three trial alignments, considering two grid cell sizes (1.0 and 2.0 Å. The QSAR equations, generated by a combined scheme of genetic algorithms (GA and partial least squares (PLS regression, were evaluated by “leave-one-out” cross-validation, using a training set of 41 compounds. External validation was performed using a test set of 13 compounds. The obtained 4D-QSAR models are in agreement with the proposed mechanism of action for raloxifene. This study allowed a quantitative prediction of compounds’ potency and supported the design of new raloxifene analogs.

  11. The QSAR study of flavonoid-metal complexes scavenging rad OH free radical

    Science.gov (United States)

    Wang, Bo-chu; Qian, Jun-zhen; Fan, Ying; Tan, Jun

    2014-10-01

    Flavonoid-metal complexes have antioxidant activities. However, quantitative structure-activity relationships (QSAR) of flavonoid-metal complexes and their antioxidant activities has still not been tackled. On the basis of 21 structures of flavonoid-metal complexes and their antioxidant activities for scavenging rad OH free radical, we optimised their structures using Gaussian 03 software package and we subsequently calculated and chose 18 quantum chemistry descriptors such as dipole, charge and energy. Then we chose several quantum chemistry descriptors that are very important to the IC50 of flavonoid-metal complexes for scavenging rad OH free radical through method of stepwise linear regression, Meanwhile we obtained 4 new variables through the principal component analysis. Finally, we built the QSAR models based on those important quantum chemistry descriptors and the 4 new variables as the independent variables and the IC50 as the dependent variable using an Artificial Neural Network (ANN), and we validated the two models using experimental data. These results show that the two models in this paper are reliable and predictable.

  12. QSAR Study on the anti-tumor activity of levofloxacin-thiadiazole HDACi conjugates

    Science.gov (United States)

    Tang, Ziqiang; Feng, Hui; Chen, Yan; Yue, Wei; Feng, Changjun

    2017-12-01

    A molecular electronegativity distance vector(M t) based on 13atomic types is used to describe the structures of 19 conjugates(LHCc) of levofloxacin-thiadiazole HDAC inhibitor(HDACi) and related to the anti-tumor activity (M F and P C) of LHCc against MCF-7 and PC-3. The quantitative structure-activity relationships (QSAR) was established by using leaps-and-bounds regression analysis for the anti-tumor activities (M F and P C) of 19 above compounds to MCF-7and PC-3 along with the M t. The correlation coefficients (R 2) and the leave-one-out (LOO) cross validation R cv 2 for the M F and P C models were 0.792 and 0.679; 0.773 and 0.565, respectively. The QSAR models have favorable correlation, as well as robustness and good prediction capability by R 2, F, R cv 2, A IC F IT V IF tests. The results indicate that the molecular structural units: -CHg-(g=1, 2), -NH2, -NH-,-OH, O=, -O-, -S- and -X are main factors which can affect the anti-tumor activity M F and PC bioactivities of these compounds directly.

  13. QSAR Studies of 6-Amino Uracil Base Analogues: A Thymidine Phosphorylase Inhibitor in Cancer Therapy

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    Surya Prakash B. N. Gupta

    2008-01-01

    Full Text Available A novel series of 6-amino uracil base analogue were synthesized. QSAR study was used to relate the selective nonsubstrate inhibitory activity of 6-amino uracil base analogue with various physicochemical descriptors. Stepwise multiple regression analysis was performed to find out the correlation between various physicochemical descriptors and biological activity of the compounds by using Openstat 2 version 6.5.1 and valstat statistical software. Out of the several equations developed, the best equation having the highest significance was selected for further study. The equation is able to explain 60% of total variance and are more than 95% significant as revealed by the F value.

  14. Quantitative structure-activity relationship (QSAR) prediction of (eco)toxicity of short aliphatic protic ionic liquids.

    Science.gov (United States)

    Peric, Brezana; Sierra, Jordi; Martí, Esther; Cruañas, Robert; Garau, Maria Antonia

    2015-05-01

    Ionic liquids (ILs) are considered as a group of very promising compounds due to their excellent properties (practical non-volatility, high thermal stability and very good and diverse solving capacity). The ILs have a good prospect of replacing traditional organic solvents in vast variety of applications. However, the complete information on their environmental impact is still not available. There is also an enormous number of possible combinations of anions and cations which can form ILs, the fact that requires a method allowing the prediction of toxicity of existing and potential ILs. In this study, a group contribution QSAR model has been used in order to predict the (eco)toxicity of protic and aprotic ILs for five tests (Microtox®, Pseudokirchneriella subcapitata and Lemna minor growth inhibition test, and Acetylcholinestherase inhibition and Cell viability assay with IPC-81 cells). The predicted and experimental toxicity are well correlated. A prediction of EC50 for these (eco)toxicity tests has also been made for eight representatives of the new family of short aliphatic protic ILs, whose toxicity has not been determined experimentally to date. The QSAR model applied in this study can allow the selection of potentially less toxic ILs amongst the existing ones (e.g. in the case of aprotic ILs), but it can also be very helpful in directing the synthesis efforts toward developing new "greener" ILs respectful with the environment (e.g. short aliphatic protic ILs). Copyright © 2015 Elsevier Inc. All rights reserved.

  15. QSAR studies for the acute toxicity of nitrobenzenes to the Tetrahymena pyriformis

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    Wang Dan-Dan

    2014-01-01

    Full Text Available Quantitative structure-activity relationship (QSAR models play a key role in finding the relationship between molecular structures and the toxicity of nitrobenzenes to Tetrahymena pyriformis. In this work, genetic algorithm, along with partial least square (GA-PLS was employed to select optimal subset of descriptors that have significant contribution to the toxicity of nitrobenzenes to Tetrahymena pyriformis. A set of five descriptors, namely G2, HOMT, G(Cl…Cl, Mor03v and MAXDP, was used for the prediction of the toxicity of 45 nitrobenzene derivatives and then were used to build the model by multiple linear regression (MLR method. It turned out that the built model, whose stability was confirmed using the leave-one-out validation and external validation test, showed high statistical significance (R2=0.963, Q2LOO=0.944. Moreover, Y-scrambling test indicated there was no chance correlation in this model.

  16. Combined Pharmacophore Modeling, 3D-QSAR, Homology Modeling and Docking Studies on CYP11B1 Inhibitors

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    Rui Yu

    2015-01-01

    Full Text Available The mitochondrial cytochrome P450 enzymes inhibitor steroid 11β-hydroxylase (CYP11B1 can decrease the production of cortisol. Therefore, these inhibitors have an effect in the treatment of Cushing’s syndrome. A pharmacophore model generated by Genetic Algorithm with Linear Assignment for Hypermolecular Alignment of Datasets (GALAHAD was used to align the compounds and perform comparative molecular field analysis (CoMFA with Q2 = 0.658, R2 = 0.959. The pharmacophore model contained six hydrophobic regions and one acceptor atom, and electropositive and bulky substituents would be tolerated at the A and B sites, respectively. A three-dimensional quantitative structure-activity relationship (3D-QSAR study based on the alignment with the atom root mean square (RMS was applied using comparative molecular field analysis (CoMFA with Q2 = 0.666, R2 = 0.978, and comparative molecular similarity indices analysis (CoMSIA with Q2 = 0.721, R2 = 0.972. These results proved that all the models have good predictability of the bioactivities of inhibitors. Furthermore, the QSAR models indicated that a hydrogen bond acceptor substituent would be disfavored at the A and B groups, while hydrophobic groups would be favored at the B site. The three-dimensional (3D model of the CYP11B1 was generated based on the crystal structure of the CYP11B2 (PDB code 4DVQ. In order to probe the ligand-binding modes, Surflex-dock was employed to dock CYP11B1 inhibitory compounds into the active site of the receptor. The docking result showed that the imidazolidine ring of CYP11B1 inhibitors form H bonds with the amino group of residue Arg155 and Arg519, which suggested that an electronegative substituent at these positions could enhance the activities of compounds. All the models generated by GALAHAD QSAR and Docking methods provide guidance about how to design novel and potential drugs for Cushing’s syndrome treatment.

  17. Development of QSAR model for immunomodulatory activity of natural coumarinolignoids

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    Dharmendra K Yadav

    2010-07-01

    Full Text Available Dharmendra K Yadav, Abha Meena, Ankit Srivastava, D Chanda, Feroz Khan, SK ChattopadhyayMetabolic and Structural Biology Department, Central Institute of Medicinal and Aromatic Plants, Council of Scientific and Industrial Research, PO-CIMAP, IndiaAbstract: Immunomodulation is the process of alteration in immune response due to foreign intrusion of molecules inside the body. Along with the available drugs, a large number of herbal drugs are promoted in traditional Indian treatments, for their immunomodulating activity. Natural coumarinolignoids isolated from the seeds of Cleome viscose have been recognized as having hepatoprotective action and have recently been tested preclinically for their immunomodulatory activity affecting both cell-mediated and humoral immune response. To explore the immunomodulatory compound from derivatives of coumarinolignoids, a quantitative structure activity relationship (QSAR and molecular docking studies were performed. Theoretical results are in accord with the in vivo experimental data studied on Swiss albino mice. Immunostimulatory activity was predicted through QSAR model, developed by forward feed multiple linear regression method with leave-one-out approach. Relationship correlating measure of QSAR model was 99% (R2 = 0.99 and predictive accuracy was 96% (RCV2 = 0.96. QSAR studies indicate that dipole moment, steric energy, amide group count, lambda max (UV-visible, and molar refractivity correlates well with biological activity, while decrease in dipole moment, steric energy, and molar refractivity has negative correlation. Docking studies also showed strong binding affinity to immunomodulatory receptors.Keywords: coumarinolignoids, immunomodulation, docking, QSAR, regression model

  18. Quantitative structure-activity relationship (QSAR) for a series of novel cannabinoid derivatives using descriptors derived from semi-empirical quantum-chemical calculations.

    Science.gov (United States)

    Ferreira, Antonio M; Krishnamurthy, Mathangi; Moore, Bob M; Finkelstein, David; Bashford, Donald

    2009-03-15

    Recent work implicating the cannabinoid receptors in a wide range of human pathologies has intensified the need for reliable QSAR models for drug discovery and lead optimization. Predicting the ligand selectivity of the cannabinoid CB(1) and CB(2) receptors in the absence of generally accepted models for their structures requires a ligand-based approach, which makes such studies ideally suited for quantum-chemical treatments. We present a QSAR model for ligand-receptor interactions based on quantum-chemical descriptors (an eQSAR) obtained from PM3 semi-empirical calculations for a series of phenyl-substituted cannabinoids based on a ligand with known in vivo activity against glioma [Duntsch, C.; Divi, M. K.; Jones, T.; Zhou, Q.; Krishnamurthy, M.; Boehm, P.; Wood, G.; Sills, A.; Moore. B. M., II. J. Neuro-Oncol., 2006, 77, 143] and a set of structurally similar adamantyl-substituted cannabinoids. A good model for CB(2) inhibition (R(2)=0.78) has been developed requiring only four explanatory variables derived from semi-empirical results. The role of the ligand dipole moment is discussed and we propose that the CB(2) binding pocket likely possesses a significant electric field. Describing the affinities with respect to the CB(1) receptor was not possible with the current set of ligands and descriptors, although the attempt highlighted some important points regarding the development of QSAR models.

  19. Quantitative structure activity relationship study of p38α MAP kinase inhibitors

    OpenAIRE

    Pourbasheer, Eslam; Ahmadpour, Sajjad; Zare-Dorabei, Rohollah; Nekoei, Mehdi

    2017-01-01

    The quantitative structure activity relationship (QSAR) of the novel pyrazole derivatives as inhibitors of p38α mitogen activated protein (MAP) kinase was studied. The suitable set of the molecular descriptors was calculated and the important descriptors using the variable selections of the stepwise (SW) and the genetic algorithm (GA) were selected. The predictive quality of the QSAR models was tested for an external set of nine compounds, randomly chosen out of 44 compounds. A comparison bet...

  20. A new computer program for QSAR-analysis: ARTE-QSAR.

    Science.gov (United States)

    Van Damme, Sofie; Bultinck, Patrick

    2007-08-01

    A new computer program has been designed to build and analyze quantitative-structure activity relationship (QSAR) models through regression analysis. The user is provided with a range of regression and validation techniques. The emphasis of the program lies mainly in the validation of QSAR models in chemical applications. ARTE-QSAR produces an easy interpretable output from which the user can conclude if the obtained model is suitable for prediction and analysis.

  1. QSAR MODELING OF ANTIBACTERIAL ACTIVITY OF SOME BENZIMIDAZOLE DERIVATIVES

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    SANJA O. PODUNAVAC-KUZMANOVIĆ

    2011-03-01

    Full Text Available A quantitative structure-activity relationship (QSAR study has been carried out for a training set of 12 benzimidazole derivatives to correlate and predict the antibacterial activity of studied compounds against Gram-negative bacteria Pseudomonas aeruginosa. Multiple linear regression was used to select the descriptors and to generate the best prediction model that relates the structural features to inhibitory activity. The predictivity of the model was estimated by cross-validation with the leave-one-out method. Our results suggest a QSAR model based on the following descriptors: parameter of lipophilicity (logP and hydration energy (HE. Good agreement between experimental and predicted inhibitory values, obtained in the validation procedure, indicated the good quality of the generated QSAR model.

  2. Organic Compounds Based on (E-N-Aryl-2-ethene-sulfonamide as Microtubule Targeted Agents in Prostate Cancer: QSAR Study

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    El Ghalia Hadaji

    2017-01-01

    Full Text Available (E-N-Aryl-2-ethene-sulfonamide and its derivatives are potent anticancer agents; these compounds inhibit cancer cells proliferation. A study of quantitative structure-activity relationship (QSAR has been applied on 40 compounds based on (E-N-Aryl-2-ethene-sulfonamide, in order to predict their anticancer biological activity. The principal components analysis is used for minimizing the base matrix and the multiple linear regression (MLR and multiple nonlinear regression have been used to design the relationships between the molecular descriptor and anticancer properties of the sulfonamide derivatives. The validation of the models MLR and MNLR has been done by dividing the dataset into training and test set, the external validation of multiple correlation coefficients was RpIC50 = 0.81 for MLR and RpIC50 = 0.91 for MNLR. The artificial neural network (ANN showed a correlation coefficient close to 0.96, which concluded that this latter model is more effective and much better than the other models. This obtained model (ANN has been confirmed by two methods of LOO cross-validation and scrambling (or Y-randomization. The high correlation between experimental and predicted activity values was observed, indicating the validation and the good quality of the derived QSAR model.

  3. Combined molecular modelling and 3D-QSAR study for understanding the inhibition of NQO1 by heterocyclic quinone derivatives.

    Science.gov (United States)

    López-Lira, Claudia; Alzate-Morales, Jans H; Paulino, Margot; Mella-Raipán, Jaime; Salas, Cristian O; Tapia, Ricardo A; Soto-Delgado, Jorge

    2017-06-23

    A combination of three-dimensional quantitative structure-activity relationship (3D-QSAR), and molecular modelling methods were used to understand the potent inhibitory NAD(P)H:quinone oxidoreductase 1 (NQO1) activity of a set of 52 heterocyclic quinones. Molecular docking results indicated that some favourable interactions of key amino acid residues at the binding site of NQO1 with these quinones would be responsible for an improvement of the NQO1 activity of these compounds. The main interactions involved are hydrogen bond of the amino group of residue Tyr128, π-stacking interactions with Phe106 and Phe178, and electrostatic interactions with flavin adenine dinucleotide (FADH) cofactor. Three models were prepared by 3D-QSAR analysis. The models derived from Model I and Model III, shown leave-one-out cross-validation correlation coefficients (q2LOO ) of .75 and .73 as well as conventional correlation coefficients (R2 ) of .93 and .95, respectively. In addition, the external predictive abilities of these models were evaluated using a test set, producing the predicted correlation coefficients (r2pred ) of .76 and .74, respectively. The good concordance between the docking results and 3D-QSAR contour maps provides helpful information about a rational modification of new molecules based in quinone scaffold, in order to design more potent NQO1 inhibitors, which would exhibit highly potent antitumor activity. © 2017 John Wiley & Sons A/S.

  4. QSAR models for prediction study of HIV protease inhibitors using support vector machines, neural networks and multiple linear regression

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    Rachid Darnag

    2017-02-01

    Full Text Available Support vector machines (SVM represent one of the most promising Machine Learning (ML tools that can be applied to develop a predictive quantitative structure–activity relationship (QSAR models using molecular descriptors. Multiple linear regression (MLR and artificial neural networks (ANNs were also utilized to construct quantitative linear and non linear models to compare with the results obtained by SVM. The prediction results are in good agreement with the experimental value of HIV activity; also, the results reveal the superiority of the SVM over MLR and ANN model. The contribution of each descriptor to the structure–activity relationships was evaluated.

  5. Hyaluronidase Inhibitory Activity of Pentacylic Triterpenoids from Prismatomeris tetrandra (Roxb. K. Schum: Isolation, Synthesis and QSAR Study

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    Nor Hayati Abdullah

    2016-02-01

    Full Text Available The mammalian hyaluronidase degrades hyaluronic acid by the cleavage of the β-1,4-glycosidic bond furnishing a tetrasaccharide molecule as the main product which is a highly angiogenic and potent inducer of inflammatory cytokines. Ursolic acid 1, isolated from Prismatomeris tetrandra, was identified as having the potential to develop inhibitors of hyaluronidase. A series of ursolic acid analogues were either synthesized via structure modification of ursolic acid 1 or commercially obtained. The evaluation of the inhibitory activity of these compounds on the hyaluronidase enzyme was conducted. Several structural, topological and quantum chemical descriptors for these compounds were calculated using semi empirical quantum chemical methods. A quantitative structure activity relationship study (QSAR was performed to correlate these descriptors with the hyaluronidase inhibitory activity. The statistical characteristics provided by the best multi linear model (BML (R2 = 0.9717, R2cv = 0.9506 indicated satisfactory stability and predictive ability of the developed model. The in silico molecular docking study which was used to determine the binding interactions revealed that the ursolic acid analog 22 had a strong affinity towards human hyaluronidase.

  6. Rationalizing fragment based drug discovery for BACE1: insights from FB-QSAR, FB-QSSR, multi objective (MO-QSPR) and MIF studies

    Science.gov (United States)

    Manoharan, Prabu; Vijayan, R. S. K.; Ghoshal, Nanda

    2010-10-01

    The ability to identify fragments that interact with a biological target is a key step in FBDD. To date, the concept of fragment based drug design (FBDD) is increasingly driven by bio-physical methods. To expand the boundaries of QSAR paradigm, and to rationalize FBDD using In silico approach, we propose a fragment based QSAR methodology referred here in as FB-QSAR. The FB-QSAR methodology was validated on a dataset consisting of 52 Hydroxy ethylamine (HEA) inhibitors, disclosed by GlaxoSmithKline Pharmaceuticals as potential anti-Alzheimer agents. To address the issue of target selectivity, a major confounding factor in the development of selective BACE1 inhibitors, FB-QSSR models were developed using the reported off target activity values. A heat map constructed, based on the activity and selectivity profile of the individual R-group fragments, and was in turn used to identify superior R-group fragments. Further, simultaneous optimization of multiple properties, an issue encountered in real-world drug discovery scenario, and often overlooked in QSAR approaches, was addressed using a Multi Objective (MO-QSPR) method that balances properties, based on the defined objectives. MO-QSPR was implemented using Derringer and Suich desirability algorithm to identify the optimal level of independent variables ( X) that could confer a trade-off between selectivity and activity. The results obtained from FB-QSAR were further substantiated using MIF (Molecular Interaction Fields) studies. To exemplify the potentials of FB-QSAR and MO-QSPR in a pragmatic fashion, the insights gleaned from the MO-QSPR study was reverse engineered using Inverse-QSAR in a combinatorial fashion to enumerate some prospective novel, potent and selective BACE1 inhibitors.

  7. Theoretical study of GSK-3α: neural networks QSAR studies for the design of new inhibitors using 2D descriptors.

    Science.gov (United States)

    García, Isela; Fall, Yagamare; García-Mera, Xerardo; Prado-Prado, Francisco

    2011-11-01

    Glycogen synthase kinase-3 (GSK-3) targets encompass proteins implicated in AD and neurological disorders. The functions of GSK-3 and its implication in various human diseases have triggered an active search for potent and selective GSK-3 inhibitors. In this sense, QSAR could play an important role in studying these GSK-3 inhibitors. For this reason, we developed QSAR models for GSK-3α, linear discriminant analysis (LDA), and artificial neural networks (ANNs) from nearly 50,000 cases with more than 700 different GSK-3α inhibitors obtained from ChEMBL database server; in total we used more than 20,000 different molecules to develop the QSAR models. The model correctly classified 237 out of 275 active compounds (86.2%) and 14,870 out of 15,970 non-active compounds (93.2%) in the training series. The overall training performance was 93.0%. Validation of the model was carried out using an external predicting series. In these series, the model classified correctly 458 out of 549 (83.4%) compounds and 29,637 out of 31,927 non-active compounds (83.4%). The overall predictability performance was 92.7%. In this study, we propose three types of non-linear ANN as alternative to already existing models, such as LDA. Linear neural network: LNN: 236:236-1-1:1 which had an overall training performance of 96% proved to be the best model. In addition, we did a study of the different fragments of the molecules of the database to see which fragments had more influence in the activity. This can help design new inhibitors of GSK-3α. This study reports the attempts to calculate, within a unified framework probabilities of GSK-3α inhibitors against different molecules found in the literature.

  8. QSAR study of HCV NS5B polymerase inhibitors using the genetic algorithm-multiple linear regression (GA-MLR).

    Science.gov (United States)

    Rafiei, Hamid; Khanzadeh, Marziyeh; Mozaffari, Shahla; Bostanifar, Mohammad Hassan; Avval, Zhila Mohajeri; Aalizadeh, Reza; Pourbasheer, Eslam

    2016-01-01

    Quantitative structure-activity relationship (QSAR) study has been employed for predicting the inhibitory activities of the Hepatitis C virus (HCV) NS5B polymerase inhibitors . A data set consisted of 72 compounds was selected, and then different types of molecular descriptors were calculated. The whole data set was split into a training set (80 % of the dataset) and a test set (20 % of the dataset) using principle component analysis. The stepwise (SW) and the genetic algorithm (GA) techniques were used as variable selection tools. Multiple linear regression method was then used to linearly correlate the selected descriptors with inhibitory activities. Several validation technique including leave-one-out and leave-group-out cross-validation, Y-randomization method were used to evaluate the internal capability of the derived models. The external prediction ability of the derived models was further analyzed using modified r(2), concordance correlation coefficient values and Golbraikh and Tropsha acceptable model criteria's. Based on the derived results (GA-MLR), some new insights toward molecular structural requirements for obtaining better inhibitory activity were obtained.

  9. Theoretical study on modeling and prediction of optical rotation for biodegradable polymers containing α-amino acids using QSAR approaches.

    Science.gov (United States)

    Mallakpour, Shadpour; Hatami, Mehdi; Golmohammadi, Hassan

    2011-07-01

    The main purpose of the present study was modeling and prediction of the optical rotation ([M](D)) of some biodegradable polymers containing α-amino acids using quantitative structure-activity relationship (QSAR) approaches. In order to attain this goal, the optical rotation of a collection of 53 polymers was selected as a data set. The data set was randomly divided into three sections, training, test and external validation sets. By using dragon software, various descriptors were calculated for all molecules in the data set. The important descriptors were selected applying genetic algorithm-partial least squares (GA-PLS) method. Then an artificial neural network (ANN) was written with MATLAB 7 and used these descriptors as inputs and its output was optical rotation of desired polymers. Then, the constructed network was used for the prediction of ([M](D)) values of validation set. The squared correlation coefficient R² values of the ANN model for the training, test and validation sets were 0.998, 0.996 and 0.996 respectively. The results showed the ability of developed ANN to predict optical rotation of various polymers.

  10. Synthesis and in Vitro Antioxidant Activity Evaluation of 3-Carboxycoumarin Derivatives and QSAR Study of Their DPPH• Radical Scavenging Activity

    Directory of Open Access Journals (Sweden)

    Maria Teresa Sumaya-Martínez

    2012-12-01

    Full Text Available The in vitro antioxidant activities of eight 3-carboxycoumarin derivatives were assayed by the quantitative 1,1-diphenyl-2-picrylhydrazil (DPPH• radical scavenging activity method. 3-Acetyl-6-hydroxy-2H-1-benzopyran-2-one (C1 and ethyl 6-hydroxy-2-oxo-2H-1-benzopyran-3-carboxylate (C2 presented the best radical-scavenging activity. A quantitative structure-activity relationship (QSAR study was performed and correlated with the experimental DPPH• scavenging data. We used structural, geometrical, topological and quantum-chemical descriptors selected with Genetic Algorithms in order to determine which of these parameters are responsible of the observed DPPH• radical scavenging activity. We constructed a back propagation neural network with the hydrophilic factor (Hy descriptor to generate an adequate architecture of neurons for the system description. The mathematical model showed a multiple determination coefficient of 0.9196 and a root mean squared error of 0.0851. Our results shows that the presence of hydroxyl groups on the ring structure of 3-carboxy-coumarins are correlated with the observed DPPH• radical scavenging activity effects.

  11. Multivariate QSAR study on the antimutagenic activity of flavonoids against 3-NFA on Salmonella typhimurium TA98.

    Science.gov (United States)

    Borges de Melo, Eduardo; Ataide Martins, João Paulo; Marinho Jorge, Teresa Cristina; Friozi, Marcelo Couto; Castro Ferreira, Márcia Miguel

    2010-10-01

    A quantitative structure-activity relationship (QSAR) study of twenty flavonoid derivatives with antimutagenic activity against 3-nitrofluoranthene (3-NFA) was performed by Partial Least Squares (PLS), using Ordered Predictors Selection (OPS) algorithm for variable selection. Four descriptors (PJI2, Mor27m, G1e and R4u+) were selected and a good model (n = 19; R(2) = 0.747; SEC = 0.332; PRESS(cal) = 1.768; F((2,27)) = 23.585; Q(LOO)(2) = 0.590; SEV = 0.388; PRESS(val) = 2.858; R(pred)(2) = 0.591; SEP = 0.394; ARE(pred) = 5.230%; k = 1.005; k' = 0.990; |R(02) - R'(02)| = 0.109) was built with two latent variables describing 83.410% of the original information. Leave-N-out cross validation (LNO) and y-randomization were performed in order to confirm the robustness of the model. The topological descriptors selected indicate that the antimutagenic activity against 3-NFA depends on molecular size, shape and Sanderson electronegativity of flavonoids. The proposed model may provide a better understanding of the antimutagenic activity of flavonoids and can be used as a guidance for proposition of new chemopreventive agents. Copyright (c) 2010 Elsevier Masson SAS. All rights reserved.

  12. QSAR Models for Thyroperoxidase Inhibition and Screening of U.S. and EU Chemical Inventories

    DEFF Research Database (Denmark)

    Abildgaard Rosenberg, Sine; D. Watt, Eric; Judson, Richard S.

    2017-01-01

    Thyroperoxidase (TPO) is the enzyme that synthesizes thyroid hormones (THs). TPO inhibition by chemicals can result in decreased TH levels and developmental neurotoxicity, and therefore identification of TPO inhibition is of high relevance in safety evaluation of chemicals. In the present study, we...... developed two global quantitative structure-activity relationship (QSAR) models for TPO inhibition in vitro. Rigorous cross- and blinded external validations demonstrated that the first model, QSAR1, built from a training set of 877 chemicals, was robust and highly predictive with balanced accuracies of 80...... of QSAR2 identified the ten most discriminating structural features for TPO inhibition and non-inhibition, respectively. Both models were used to screen 72,524 REACH substances and 32,197 U.S. EPA substances, and QSAR2 with the expanded training set had an approximately 10% larger coverages compared...

  13. Tyrosinase Inhibitory Activity, 3D QSAR, and Molecular Docking Study of 2,5-Disubstituted-1,3,4-Oxadiazoles

    Directory of Open Access Journals (Sweden)

    Ramesh L. Sawant

    2013-01-01

    Full Text Available In continuation with our research program, in search of potent enzyme tyrosinase inhibitor, a series of synthesized 2,5-disubstituted 1,3,4-oxadiazoles have been evaluated for enzyme tyrosinase inhibitory activity. Subsequently, 3D QSAR and docking studies were performed to find optimum structural requirements for potent enzyme tyrosinase inhibitor from this series. The synthesized 20 compounds of 2,5-disubstituted-1,3,4-oxadiazole series were screened for mushroom tyrosinase inhibitory activity at various concentrations by enzyme inhibition assay. The percentage enzyme inhibition was calculated by recording absorbance at 492 nm with microplate reader. 3D QSAR and docking studies were performed using VLife MDS 3.5 software. In the series 2,5-disubstituted-1,3,4-oxadiazoles enzyme tyrosinase inhibitory activity was found to be dose dependent with maximum activity for compounds 4c, 4h, 4m, and 4r. 3D QSAR and docking studies revealed that more electropositive and less bulky substituents if placed on 1,3,4-oxadiazole nucleus may result in better tyrosinase inhibitory activity in the series.

  14. Comparative study between 3D-QSAR and Docking-Based Pharmacophore models for potent Plasomodium falciparum dihydroorotate dehydrogenase inhibitors.

    Science.gov (United States)

    Tseng, Tien-Sheng; Lee, Yu-Ching; Hsiao, Nai-Wan; Liu, Yun-Ru; Tsai, Keng-Chang

    2016-01-15

    Malaria, caused by infections of the human malaria parasites Plasmodium falciparum, is a global infectious parasitic disease. Each year, about three million people died from malaria and the majority of whom are pregnant women and young children. Recently, a number of research attempt to reduce malaria parasite resistance and the toxicity of anti-malarial drugs. Nowadays, Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH) was validated as a potent drug target to inhibit malarial activity by blocking pyrimidine biosynthesis. In this study, we employed 3D-QSAR Pharmacophore Generation and Docking-Based Pharmacophore Development to build the pharmacophore by using the collected 67 effective inhibitors against PfDHODH. 3D-QSAR Pharmacophore model, Hypo1, shows the high correlation coefficient (0.935), the lowest RMS deviation (2.15), the predicting accuracy of successful rates to training set (89.4%) and test set compounds (72.4%), respectively, revealing favorable predictive ability and is a reliable for further study. Additionally, Docking-Based Pharmacophore model, DBP-All255, exhibits comparable predictive capability to that of Hypo1, while DBP-Top1 shows poor statistical significance. This study reveals pharmacophore features of Hypo1, built by 3D-QSAR Pharmacophore Generation, are well-complementary to the functional residues in the active site of PfDHODH and is of great reliable for database screening. Copyright © 2015 Elsevier Ltd. All rights reserved.

  15. Docking and 3-D QSAR studies on the binding of tetrahydropyrimid-2-one HIV-1 protease inhibitors

    Science.gov (United States)

    Rao, Shashidhar N.; Balaji, Govardhan A.; Balaji, Vitukudi N.

    2013-06-01

    We present molecular docking and 3-D QSAR studies on a series of tetrahydropyrimid-2-one HIV-1 protease inhibitors whose binding affinities to the enzyme span nearly 6 orders of magnitude. The docking investigations have been carried out with Surflex (GEOM, GEOMX) and Glide (SP and XP) methodologies available through Tripos and Schrodinger suite of tools in the context of Sybyl-X and Maestro interfaces, respectively. The alignments for 3-D QSAR studies were obtained by using the automated Surflex-SIM methodology in Sybyl-X and the analyses were performed using the CoMFA and CoMSIA methods. Additionally, the top-ranked poses obtained from various docking protocols were also employed to generate CoMFA and CoMSIA models to evaluate the qualitative consistency of the docked models with experimental data. Our studies demonstrate that while there are a number of common features in the docked models obtained from Surflex-dock and Glide methodologies, the former sets of models are generally better correlated with deduced experimental binding modes based on the X-ray structures of known HIV-1 protease complexes with cyclic ureas. The urea moiety common to all the ligands are much more tightly aligned in Surflex docked structures than in the models obtained from Glide SP and XP dockings. The 3-D QSAR models are qualitatively and quantitatively similar to those previously reported, suggesting the utility of automatically generated alignments from Surflex-SIM methodology.

  16. QSAR model reproducibility and applicability: a case study of rate constants of hydroxyl radical reaction models applied to polybrominated diphenyl ethers and (benzo-)triazoles.

    Science.gov (United States)

    Roy, Partha Pratim; Kovarich, Simona; Gramatica, Paola

    2011-08-01

    The crucial importance of the three central OECD principles for quantitative structure-activity relationship (QSAR) model validation is highlighted in a case study of tropospheric degradation of volatile organic compounds (VOCs) by OH, applied to two CADASTER chemical classes (PBDEs and (benzo-)triazoles). The application of any QSAR model to chemicals without experimental data largely depends on model reproducibility by the user. The reproducibility of an unambiguous algorithm (OECD Principle 2) is guaranteed by redeveloping MLR models based on both updated version of DRAGON software for molecular descriptors calculation and some freely available online descriptors. The Genetic Algorithm has confirmed its ability to always select the most informative descriptors independently on the input pool of variables. The ability of the GA-selected descriptors to model chemicals not used in model development is verified by three different splittings (random by response, K-ANN and K-means clustering), thus ensuring the external predictivity of the new models, independently of the training/prediction set composition (OECD Principle 5). The relevance of checking the structural applicability domain becomes very evident on comparing the predictions for CADASTER chemicals, using the new models proposed herein, with those obtained by EPI Suite. Copyright © 2011 Wiley Periodicals, Inc.

  17. In Silico Exploration of 1,7-Diazacarbazole Analogs as Checkpoint Kinase 1 Inhibitors by Using 3D QSAR, Molecular Docking Study, and Molecular Dynamics Simulations

    Directory of Open Access Journals (Sweden)

    Xiaodong Gao

    2016-05-01

    Full Text Available Checkpoint kinase 1 (Chk1 is an important serine/threonine kinase with a self-protection function. The combination of Chk1 inhibitors and anti-cancer drugs can enhance the selectivity of tumor therapy. In this work, a set of 1,7-diazacarbazole analogs were identified as potent Chk1 inhibitors through a series of computer-aided drug design processes, including three-dimensional quantitative structure–activity relationship (3D-QSAR modeling, molecular docking, and molecular dynamics simulations. The optimal QSAR models showed significant cross-validated correlation q2 values (0.531, 0.726, fitted correlation r2 coefficients (higher than 0.90, and standard error of prediction (less than 0.250. These results suggested that the developed models possess good predictive ability. Moreover, molecular docking and molecular dynamics simulations were applied to highlight the important interactions between the ligand and the Chk1 receptor protein. This study shows that hydrogen bonding and electrostatic forces are key interactions that confer bioactivity.

  18. 3D-QSAR and Docking Studies of a Series of β-Carboline Derivatives as Antitumor Agents of PLK1

    Directory of Open Access Journals (Sweden)

    Jahan B. Ghasemi

    2014-01-01

    Full Text Available An alignment-free, three dimensional quantitative structure-activity relationship (3D-QSAR analysis has been performed on a series of β-carboline derivatives as potent antitumor agents toward HepG2 human tumor cell lines. A highly descriptive and predictive 3D-QSAR model was obtained through the calculation of alignment-independent descriptors (GRIND descriptors using ALMOND software. For a training set of 30 compounds, PLS analyses result in a three-component model which displays a squared correlation coefficient (r2 of 0.957 and a standard deviation of the error of calculation (SDEC of 0.116. Validation of this model was performed using leave-one-out, q2loo of 0.85, and leave-multiple-out. This model gives a remarkably high r2pred(0.66 for a test set of 10 compounds. Docking studies were performed to investigate the mode of interaction between β-carboline derivatives and the active site of the most probable anticancer receptor, polo-like kinase protein.

  19. Synthesis and QSAR study of novel α-methylene-γ-butyrolactone derivatives as antifungal agents.

    Science.gov (United States)

    Wu, Yong-Ling; Wang, De-Long; Guo, En-Hui; Song, Shuang; Feng, Jun-Tao; Zhang, Xing

    2017-03-01

    Thirty-six new α-benzylidene-γ-lactone compounds based α-methylene-γ-butyrolactone substructure were prepared and characterized by spectroscopic analysis. All compounds were evaluated for antifungal activities in vitro against six plant pathogenic fungi and the half maximal inhibitory concentration (IC 50 ) against Botrytis cinerea and Colletotrichum lagenarium were investigated. Compounds 5c-3 and 5c-5 with the halogen atom exhibited excellent fungicidal activity against B. cinerea (IC 50 =22.91, 18.89μM). The structure-activity relationships (SARs) analysis indicated that the derivatives with electron-withdrawing substituents at the meta- or para-positions improves the activity. Via the heuristic method, the generated quantitative structure-activity relationship (QSAR) model (R 2 =0.961) revealed a strong correlation of antifungal activity against B. cinerea with molecular structures of these compounds. Meanwhile, the cytotoxicity of 20 representative derivatives was tested in the human tumor cells line (HepG2) and the hepatic L02 cells line, the result indicated that the synthesized compounds showed significant inhibitory activity and limited selectivity. Compound 5c-5 has the highest fungicidal activity with IC 50 =18.89μM (against B. cinerea.) but low cytotoxicity with IC 50 =35.4μM (against HepG2 cell line) and IC 50 =68.8μM (against Hepatic L02 cell line). These encouraging results can be providing an alternative, promising use of α-benzylidene-γ-lactone through the design and exploration of eco-friendly fungicides with low toxicity and high efficiency. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Design, synthesis, α-glucosidase inhibitory activity, molecular docking and QSAR studies of benzimidazole derivatives

    Science.gov (United States)

    Dinparast, Leila; Valizadeh, Hassan; Bahadori, Mir Babak; Soltani, Somaieh; Asghari, Behvar; Rashidi, Mohammad-Reza

    2016-06-01

    In this study the green, one-pot, solvent-free and selective synthesis of benzimidazole derivatives is reported. The reactions were catalyzed by ZnO/MgO containing ZnO nanoparticles as a highly effective, non-toxic and environmentally friendly catalyst. The structure of synthesized benzimidazoles was characterized using spectroscopic technics (FT-IR, 1HNMR, 13CNMR). Synthesized compounds were evaluated for their α-glucosidase inhibitory potential. Compounds 3c, 3e, 3l and 4n were potent inhibitors with IC50 values ranging from 60.7 to 168.4 μM. In silico studies were performed to explore the binding modes and interactions between enzyme and synthesized benzimidazoles. Developed linear QSAR model based on density and molecular weight could predict bioactivity of newly synthesized compounds well. Molecular docking studies revealed the availability of some hydrophobic interactions. In addition, the bioactivity of most potent compounds had good correlation with estimated free energy of binding (ΔGbinding) which was calculated according to docked best conformations.

  1. 3D-QSAR and molecular docking studies on designing inhibitors of the hepatitis C virus NS5B polymerase

    Science.gov (United States)

    Li, Wenlian; Si, Hongzong; Li, Yang; Ge, Cuizhu; Song, Fucheng; Ma, Xiuting; Duan, Yunbo; Zhai, Honglin

    2016-08-01

    Viral hepatitis C infection is one of the main causes of the hepatitis after blood transfusion and hepatitis C virus (HCV) infection is a global health threat. The HCV NS5B polymerase, an RNA dependent RNA polymerase (RdRp) and an essential role in the replication of the virus, has no functional equivalent in mammalian cells. So the research and development of efficient NS5B polymerase inhibitors provides a great strategy for antiviral therapy against HCV. A combined three-dimensional quantitative structure-activity relationship (QSAR) modeling was accomplished to profoundly understand the structure-activity correlation of a train of indole-based inhibitors of the HCV NS5B polymerase to against HCV. A comparative molecular similarity indices analysis (COMSIA) model as the foundation of the maximum common substructure alignment was developed. The optimum model exhibited statistically significant results: the cross-validated correlation coefficient q2 was 0.627 and non-cross-validated r2 value was 0.943. In addition, the results of internal validations of bootstrapping and Y-randomization confirmed the rationality and good predictive ability of the model, as well as external validation (the external predictive correlation coefficient rext2 = 0.629). The information obtained from the COMSIA contour maps enables the interpretation of their structure-activity relationship. Furthermore, the molecular docking study of the compounds for 3TYV as the protein target revealed important interactions between active compounds and amino acids, and several new potential inhibitors with higher activity predicted were designed basis on our analyses and supported by the simulation of molecular docking. Meanwhile, the OSIRIS Property Explorer was introduced to help select more satisfactory compounds. The satisfactory results from this study may lay a reliable theoretical base for drug development of hepatitis C virus NS5B polymerase inhibitors.

  2. EXPLORING THE USEFULNESS OF KEY GREEN PHYSICOCHEMICAL PROPERTIES. QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP (QSAR) FOR SOLVENTS FROM BIOMASS.

    Science.gov (United States)

    Zuriaga, Estefanía; Giner, Beatriz; Ribate, María Pilar; García, Cristina B; Lomba, Laura

    2017-12-13

    During the last decades there has been a growing interest in the development of new solvents from biomass. Some of these new solvents have been classified as green due to their renewable and sustainable source. However, characterization from the ecotoxicological and physicochemical point of view is needed in order to categorize them as green solvents. We have selected several key physicochemical properties that can reflect environmental features (density, boiling point, critical aggregation concentration and Log P) and explored their usefulness for preliminary assessing the green character of the studied solvents. Specifically, we have studied several solvents form biomass; lactate family (methyl, ethyl and butyl lactate), furfural family (furfural, 5-methylfurfural, furfuryl alcohol and tetrahydrofurfuryl alcohol) and levulinate family (methyl, ethyl and butyl levulinate). In order to fill the gaps and complete some toxicity data for the environment, we have measured the ecotoxicity using two of the most common and versatile biomodels; bacteria Vibrio fischeri and crustacean Daphnia magna for furfural and lactate derived compounds. Results indicate that solvents from biomass can be categorized as green since their toxicity for the environment is low. Finally, a QSAR study has been performed with the selected key properties and the ecotoxicological information. Despite the different structure of the chemicals under study, good correlations have been found for the studied organisms. It seems that the Log P and critical aggregation concentration (c.a.c.) carry the most part of the ecotoxic behaviour, while density and boiling point cannot reflect toxicity signals. However, these properties are rather useful for assessing the environmental final fate of the studied chemicals. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

  3. A DFT-based QSAR study on inhibition of human dihydrofolate reductase.

    Science.gov (United States)

    Karabulut, Sedat; Sizochenko, Natalia; Orhan, Adnan; Leszczynski, Jerzy

    2016-11-01

    Diaminopyrimidine derivatives are frequently used as inhibitors of human dihydrofolate reductase, for example in treatment of patients whose immune system are affected by human immunodeficiency virus. Forty-seven dicyclic and tricyclic potential inhibitors of human dihydrofolate reductase were analyzed using the quantitative structure-activity analysis supported by DFT-based and DRAGON-based descriptors. The developed model yielded an RMSE deviation of 1.1 a correlation coefficient of 0.81. The prediction set was characterized by R 2 =0.60 and RMSE=3.59. Factors responsible for inhibition process were identified and discussed. The resulting model was validated via cross validation and Y-scrambling procedure. From the best model, we found several mass-related descriptors and Sanderson electronegativity-related descriptors that have the best correlations with the investigated inhibitory concentration. These descriptors reflect results from QSAR studies based on characteristics of human dihydrofolate reductase inhibitors. Copyright © 2016. Published by Elsevier Inc.

  4. 3D-QSAR study and design of 4-hydroxyamino α-pyranone carboxamide analogues as potential anti-HCV agents

    Science.gov (United States)

    Li, Wenlian; Xiao, Faqi; Zhou, Mingming; Jiang, Xuejin; Liu, Jun; Si, Hongzong; Xie, Meng; Ma, Xiuting; Duan, Yunbo; Zhai, Honglin

    2016-09-01

    The three dimensional-quantitative structure activity relationship (3D-QSAR) study was performed on a series of 4-hydroxyamino α-pyranone carboxamide analogues using comparative molecular similarity indices analysis (COMSIA). The purpose of the present study was to develop a satisfactory model providing a reliable prediction based on 4-hydroxyamino α-pyranone carboxamide analogues as anti-HCV (hepatitis C virus) inhibitors. The statistical results and the results of validation of this optimum COMSIA model were satisfactory. Furthermore, analysis of the contour maps helped to provide guidelines for finding structural requirement. Therefore, the satisfactory results from this study may provide useful guidelines for drug development of anti-HCV inhibitors.

  5. Molecular docking, MM/GBSA and 3D-QSAR studies on EGFR ...

    Indian Academy of Sciences (India)

    Information rendered from 3D-QSAR model and sitemap analysis was used to optimize lead molecule to design prospective inhibitors. Improvement in EGFR binding affinity can be achieved by substitutional modification on phenyl ring attached to alkynyl group with bulkier hydrogen bond donor and acceptor substituents ...

  6. Adaptive neuro-fuzzy inference system-applied QSAR with bond dissociation energy for antioxidant activities of phenolic compounds.

    Science.gov (United States)

    Jhin, Changho; Nho, Chu Won; Hwang, Keum Taek

    2017-10-01

    The aim of this study was to develop quantitative structure-activity relationship (QSAR) models for predicting antioxidant activities of phenolic compounds. The bond dissociation energy of O-H bond (BDE) was calculated by semi-empirical quantum chemical methods. As a new parameter for QSAR models, sum of reciprocals of BDE of enol and phenol groups (X BDE ) was calculated. Significant correlations were observed between X BDE and antioxidant activities, and X BDE was introduced as a parameter for developing QSAR models. Linear regression-applied QSAR models and adaptive neuro-fuzzy inference system (ANFIS)-applied QSAR models were developed. QSAR models by both of linear regression and ANFIS achieved high prediction accuracies. Among the developed models, ANFIS-applied models achieved better prediction accuracies than linear regression-applied models. From these results, the proposed parameter of X BDE was confirmed as an appropriate variable for predicting and analysing antioxidant activities of phenolic compounds. Also, the ANFIS could be applied on QSAR models to improve prediction accuracy.

  7. Enhanced QSAR Model Performance by Integrating Structural and Gene Expression Information

    Directory of Open Access Journals (Sweden)

    Xiaohui Fan

    2013-09-01

    Full Text Available Despite decades of intensive research and a number of demonstrable successes, quantitative structure-activity relationship (QSAR models still fail to yield predictions with reasonable accuracy in some circumstances, especially when the QSAR paradox occurs. In this study, to avoid the QSAR paradox, we proposed a novel integrated approach to improve the model performance through using both structural and biological information from compounds. As a proof-of-concept, the integrated models were built on a toxicological dataset to predict non-genotoxic carcinogenicity of compounds, using not only the conventional molecular descriptors but also expression profiles of significant genes selected from microarray data. For test set data, our results demonstrated that the prediction accuracy of QSAR model was dramatically increased from 0.57 to 0.67 with incorporation of expression data of just one selected signature gene. Our successful integration of biological information into classic QSAR model provided a new insight and methodology for building predictive models especially when QSAR paradox occurred.

  8. An ensemble model of QSAR tools for regulatory risk assessment.

    Science.gov (United States)

    Pradeep, Prachi; Povinelli, Richard J; White, Shannon; Merrill, Stephen J

    2016-01-01

    Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibility with regulatory use, QSAR models should be transparent, reproducible and optimized to minimize the number of false negatives. In silico QSAR tools are gaining wide acceptance as a faster alternative to otherwise time-consuming clinical and animal testing methods. However, different QSAR tools often make conflicting predictions for a given chemical and may also vary in their predictive performance across different chemical datasets. In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns. To address these concerns, ensemble learning techniques in the machine learning paradigm can be used to integrate predictions from multiple tools. By leveraging various underlying QSAR algorithms and training datasets, the resulting consensus prediction should yield better overall predictive ability. We present a novel ensemble QSAR model using Bayesian classification. The model allows for varying a cut-off parameter that allows for a selection in the desirable trade-off between model sensitivity and specificity. The predictive performance of the ensemble model is compared with four in silico tools (Toxtree, Lazar, OECD Toolbox, and Danish QSAR) to predict carcinogenicity for a dataset of air toxins (332 chemicals) and a subset of the gold carcinogenic potency database (480 chemicals). Leave-one-out cross validation results show that the ensemble model achieves the best trade-off between sensitivity and specificity (accuracy: 83.8 % and 80.4 %, and balanced accuracy: 80.6 % and 80.8 %) and highest inter-rater agreement [kappa (κ): 0

  9. 3D-QSAR modeling and molecular docking studies on a series of 2,5 disubstituted 1,3,4-oxadiazoles

    Science.gov (United States)

    Ghaleb, Adib; Aouidate, Adnane; Ghamali, Mounir; Sbai, Abdelouahid; Bouachrine, Mohammed; Lakhlifi, Tahar

    2017-10-01

    3D-QSAR (comparative molecular field analysis (CoMFA)) and comparative molecular similarity indices analysis (CoMSIA) were performed on novel 2,5 disubstituted 1,3,4-oxadiazoles analogues as anti-fungal agents. The CoMFA and CoMSIA models using 13 compounds in the training set gives Q2 values of 0.52 and 0.51 respectively, while R2 values of 0.92. The adapted alignment method with the suitable parameters resulted in reliable models. The contour maps produced by the CoMFA and CoMSIA models were employed to determine a three-dimensional quantitative structure-activity relationship. Based on this study a set of new molecules with high predicted activities were designed. Surflex-docking confirmed the stability of predicted molecules in the receptor.

  10. Development of a QSAR model for binding of tripeptides and tripeptidomimetics to the human intestinal di-/tripeptide transporter hPEPT1

    DEFF Research Database (Denmark)

    Andersen, Rikke; Jørgensen, Flemming Steen; Olsen, Lars

    2006-01-01

    The aim of this study was to develop a three-dimensional quantitative structure-activity relationship (QSAR) model for binding of tripeptides and tripeptidomimetics to hPEPT1 based on a series of 25 diverse tripeptides.......The aim of this study was to develop a three-dimensional quantitative structure-activity relationship (QSAR) model for binding of tripeptides and tripeptidomimetics to hPEPT1 based on a series of 25 diverse tripeptides....

  11. Some Phthalocyanine and Naphthalocyanine Derivatives as Corrosion Inhibitors for Aluminium in Acidic Medium: Experimental, Quantum Chemical Calculations, QSAR Studies and Synergistic Effect of Iodide Ions

    Directory of Open Access Journals (Sweden)

    Masego Dibetsoe

    2015-08-01

    Full Text Available The effects of seven macrocyclic compounds comprising four phthalocyanines (Pcs namely 1,4,8,11,15,18,22,25-octabutoxy-29H,31H-phthalocyanine (Pc1, 2,3,9,10,16,17,23,24-octakis(octyloxy-29H,31H-phthalocyanine (Pc2, 2,9,16,23-tetra-tert-butyl-29H,31H-phthalocyanine (Pc3 and 29H,31H-phthalocyanine (Pc4, and three naphthalocyanines namely 5,9,14,18,23,27,32,36-octabutoxy-2,3-naphthalocyanine (nPc1, 2,11,20,29-tetra-tert-butyl-2,3-naphthalocyanine (nPc2 and 2,3-naphthalocyanine (nP3 were investigated on the corrosion of aluminium (Al in 1 M HCl using a gravimetric method, potentiodynamic polarization technique, quantum chemical calculations and quantitative structure activity relationship (QSAR. Synergistic effects of KI on the corrosion inhibition properties of the compounds were also investigated. All the studied compounds showed appreciable inhibition efficiencies, which decrease with increasing temperature from 30 °C to 70 °C. At each concentration of the inhibitor, addition of 0.1% KI increased the inhibition efficiency compared to the absence of KI indicating the occurrence of synergistic interactions between the studied molecules and I− ions. From the potentiodynamic polarization studies, the studied Pcs and nPcs are mixed type corrosion inhibitors both without and with addition of KI. The adsorption of the studied molecules on Al surface obeys the Langmuir adsorption isotherm, while the thermodynamic and kinetic parameters revealed that the adsorption of the studied compounds on Al surface is spontaneous and involves competitive physisorption and chemisorption mechanisms. The experimental results revealed the aggregated interactions between the inhibitor molecules and the results further indicated that the peripheral groups on the compounds affect these interactions. The calculated quantum chemical parameters and the QSAR results revealed the possibility of strong interactions between the studied inhibitors and metal surface. QSAR

  12. QSAR Study of the Inhibitors of the Acetyl-CoA Carboxylase 1 and 2 using Bayesian Regularized Genetic Neural Networks: A Comparative Study

    OpenAIRE

    Valadkhani, Abolfazl; Asadollahi-Baboli, Mohammad; Mani-Varnosfaderani, Ahmad

    2015-01-01

    Linear and non-linear quantitative structure-activity relationship (QSAR) models were presented for modeling and predicting anti-diabetic activities of a set of inhibitors of acetyl-CoA carboxylase 1 and 2 (ACC1 and ACC2). Different algorithms were utilized to choose the best variables among large numbers of descriptors and then these selected descriptors were used for non-linear (artificial neural network) and linear (multiple linear regression) modeling. The variable selection methods were ...

  13. Design, synthesis, 3D pharmacophore, QSAR, and docking studies of carboxylic acid derivatives as Histone Deacetylase inhibitors and cytotoxic agents.

    Science.gov (United States)

    Abdel-Atty, Mona M; Farag, Nahla A; Kassab, Shaymaa E; Serya, Rabah A T; Abouzid, Khaled A M

    2014-12-01

    In this study, five series of (E)-6-(4-substituted phenyl)-4-oxohex-5-enoic acids IIb-f (E), (E)-3-(4-(substituted)-phenyl)acrylic acids IIIa-g (E), 4-(4-(substituted)phenylamino)-4-oxobutanoic acids VIa,b,e, 5-(4-(substituted)phenylamino)-5-oxopentanoic acids VIIa,f and 2-[(4-(substituted)phenyl) carbamoyl]benzoic acids VIIIa,e were designed and synthesized. Selected compounds were screened in vitro for their cytotoxic effect on 60 human NCI tumor cell lines. Compound IIf (E) displayed significant inhibitory activity against NCI Non-Small Cell Lung A549/ATCC Cancer cell line (68% inhibition) and NCI-H460 Cancer cell line (66% inhibition). Moreover, the final compounds were evaluated in vitro for their cytotoxic activity on HepG2 Cancer cell line in which histone deacetylase (HDAC) is overexpressed. Compounds IIc (E), IIf (E), IIIb (E), and IIIg (E) exhibited the highest cytotoxic activity against HepG2 human cancer cell lines with IC50 ranging from 2.27 to 10.71μM. In addition, selected compounds were tested on histone deacetylase isoforms (HDAC1-11). Molecular docking simulation was also carried out for HDLP enzyme to investigate their HDAC binding affinity. In addition, generation of 3D-pharmacophore model and quantitative structure activity relationship (QSAR) models were combined to explore the structural requirements controlling the observed cytotoxic properties. Copyright © 2014 Elsevier Inc. All rights reserved.

  14. QSAR Modeling is not "Push a Button and Find a Correlation": A Case Study of Toxicity of (Benzo-)triazoles on Algae.

    Science.gov (United States)

    Gramatica, Paola; Cassani, Stefano; Roy, Partha Pratim; Kovarich, Simona; Yap, Chun Wei; Papa, Ester

    2012-12-01

    A case study of toxicity of (benzo)triazoles ((B)TAZs) to the algae Pseudokirchneriella subcapitata is used to discuss some problems and solutions in QSAR modeling, particularly in the environmental context. The relevance of data curation (not only of experimental data, but also of chemical structures and input formats for the calculation of molecular descriptors), the crucial points of QSAR model validation and the potential application for new chemicals (internal robustness, exclusion of chance correlation, external predictivity, applicability domain) are described, while developing MLR-OLS models based on molecular descriptors, calculated by various QSAR software tools (commercial DRAGON, free PaDEL-Descriptor and QSPR-THESAURUS). Additionally, the utility of consensus models is highlighted. This work summarizes a methodology for a rigorous statistical approach to obtain reliable QSAR predictions, also for a large number of (B)TAZs in the ECHA preregistration list of REACH (even if starting from limited experimental data availability), and has evidenced some ambiguities and discrepancies related to SMILES notations from different databases; furthermore it highlighted some general problems related to QSAR model generation and was useful in the implementation of the PaDEL-Descriptor software. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  15. Predictive QSAR Models for the Toxicity of Disinfection Byproducts

    Directory of Open Access Journals (Sweden)

    Litang Qin

    2017-10-01

    Full Text Available Several hundred disinfection byproducts (DBPs in drinking water have been identified, and are known to have potentially adverse health effects. There are toxicological data gaps for most DBPs, and the predictive method may provide an effective way to address this. The development of an in-silico model of toxicology endpoints of DBPs is rarely studied. The main aim of the present study is to develop predictive quantitative structure–activity relationship (QSAR models for the reactive toxicities of 50 DBPs in the five bioassays of X-Microtox, GSH+, GSH−, DNA+ and DNA−. All-subset regression was used to select the optimal descriptors, and multiple linear-regression models were built. The developed QSAR models for five endpoints satisfied the internal and external validation criteria: coefficient of determination (R2 > 0.7, explained variance in leave-one-out prediction (Q2LOO and in leave-many-out prediction (Q2LMO > 0.6, variance explained in external prediction (Q2F1, Q2F2, and Q2F3 > 0.7, and concordance correlation coefficient (CCC > 0.85. The application domains and the meaning of the selective descriptors for the QSAR models were discussed. The obtained QSAR models can be used in predicting the toxicities of the 50 DBPs.

  16. Biochemical interpretation of quantitative structure-activity relationships (QSAR) for biodegradation of N-heterocycles: a complementary approach to predict biodegradability.

    Science.gov (United States)

    Philipp, Bodo; Hoff, Malte; Germa, Florence; Schink, Bernhard; Beimborn, Dieter; Mersch-Sundermann, Volker

    2007-02-15

    Prediction of the biodegradability of organic compounds is an ecologically desirable and economically feasible tool for estimating the environmental fate of chemicals. We combined quantitative structure-activity relationships (QSAR) with the systematic collection of biochemical knowledge to establish rules for the prediction of aerobic biodegradation of N-heterocycles. Validated biodegradation data of 194 N-heterocyclic compounds were analyzed using the MULTICASE-method which delivered two QSAR models based on 17 activating (OSAR 1) and on 16 inactivating molecular fragments (GSAR 2), which were statistically significantly linked to efficient or poor biodegradability, respectively. The percentages of correct classifications were over 99% for both models, and cross-validation resulted in 67.9% (GSAR 1) and 70.4% (OSAR 2) correct predictions. Biochemical interpretation of the activating and inactivating characteristics of the molecular fragments delivered plausible mechanistic interpretations and enabled us to establish the following biodegradation rules: (1) Target sites for amidohydrolases and for cytochrome P450 monooxygenases enhance biodegradation of nonaromatic N-heterocycles. (2) Target sites for molybdenum hydroxylases enhance biodegradation of aromatic N-heterocycles. (3) Target sites for hydratation by an urocanase-like mechanism enhance biodegradation of imidazoles. Our complementary approach represents a feasible strategy for generating concrete rules for the prediction of biodegradability of organic compounds.

  17. Quantitative structure-activity relationship study of amide mosquito repellents.

    Science.gov (United States)

    Wang, P; Xu, X; Liao, S; Song, J; Fan, G; Chen, S; Wang, Z

    2017-04-01

    A quantitative structure-activity relationship (QSAR) study on 43 amide repellents was carried out by the heuristic method in order to reveal the correlations between molecular parameters of these amides and their repellency against Aedes aegypti. Sketches and optimizations of molecular structures were achieved by the Gaussian software package. Generation and screening of molecular parameters were accomplished using CODESSA 2.7.10 software. The leave-one-out method was applied for the model validation. The results showed that a four-descriptor QSAR model with r2 of 0.897 was obtained. The average r2 values of the training set and test set of the QSAR model were 0.901 and 0.863, respectively, which suggested that the stability and predictability of the model were confirmed. Analysis of the implications of the descriptors that constitute the QSAR model indicated that all the descriptors were related to the charge distribution over the molecule and affect the dipole moment of the repellents.

  18. Adaptive Neuro-Fuzzy Inference System Applied QSAR with Quantum Chemical Descriptors for Predicting Radical Scavenging Activities of Carotenoids.

    Science.gov (United States)

    Jhin, Changho; Hwang, Keum Taek

    2015-01-01

    One of the physiological characteristics of carotenoids is their radical scavenging activity. In this study, the relationship between radical scavenging activities and quantum chemical descriptors of carotenoids was determined. Adaptive neuro-fuzzy inference system (ANFIS) applied quantitative structure-activity relationship models (QSAR) were also developed for predicting and comparing radical scavenging activities of carotenoids. Semi-empirical PM6 and PM7 quantum chemical calculations were done by MOPAC. Ionisation energies of neutral and monovalent cationic carotenoids and the product of chemical potentials of neutral and monovalent cationic carotenoids were significantly correlated with the radical scavenging activities, and consequently these descriptors were used as independent variables for the QSAR study. The ANFIS applied QSAR models were developed with two triangular-shaped input membership functions made for each of the independent variables and optimised by a backpropagation method. High prediction efficiencies were achieved by the ANFIS applied QSAR. The R-square values of the developed QSAR models with the variables calculated by PM6 and PM7 methods were 0.921 and 0.902, respectively. The results of this study demonstrated reliabilities of the selected quantum chemical descriptors and the significance of QSAR models.

  19. Adaptive Neuro-Fuzzy Inference System Applied QSAR with Quantum Chemical Descriptors for Predicting Radical Scavenging Activities of Carotenoids.

    Directory of Open Access Journals (Sweden)

    Changho Jhin

    Full Text Available One of the physiological characteristics of carotenoids is their radical scavenging activity. In this study, the relationship between radical scavenging activities and quantum chemical descriptors of carotenoids was determined. Adaptive neuro-fuzzy inference system (ANFIS applied quantitative structure-activity relationship models (QSAR were also developed for predicting and comparing radical scavenging activities of carotenoids. Semi-empirical PM6 and PM7 quantum chemical calculations were done by MOPAC. Ionisation energies of neutral and monovalent cationic carotenoids and the product of chemical potentials of neutral and monovalent cationic carotenoids were significantly correlated with the radical scavenging activities, and consequently these descriptors were used as independent variables for the QSAR study. The ANFIS applied QSAR models were developed with two triangular-shaped input membership functions made for each of the independent variables and optimised by a backpropagation method. High prediction efficiencies were achieved by the ANFIS applied QSAR. The R-square values of the developed QSAR models with the variables calculated by PM6 and PM7 methods were 0.921 and 0.902, respectively. The results of this study demonstrated reliabilities of the selected quantum chemical descriptors and the significance of QSAR models.

  20. Comparative molecular field analysis (CoMFA), topomer CoMFA, and hologram QSAR studies on a series of novel HIV-1 protease inhibitors.

    Science.gov (United States)

    Heidari, Afsane; Fatemi, Mohammad H

    2017-06-01

    Comparative molecular field analysis (CoMFA), topomer CoMFA, and hologram QSAR as three efficient methods of QSAR have been performed on 40 newly synthesized inhibitors against HIV-1 protease. Molecular alignment was performed by aid of crystallographic structure of template inhibitor (indirect alignment) and also by the molecular mechanic (MM)-minimized structure. Both alignment methods produced satisfactory statistics for training set, but indirect alignment had more predictive power. Generated counter maps, especially by topomer CoMFA, give comprehensive information about structural features affecting the inhibitory activities of studied chemicals. Based on the obtained information, some new inhibitors were suggested. © 2016 John Wiley & Sons A/S.

  1. Quantitative structure-activity relationship study on BTK inhibitors by modified multivariate adaptive regression spline and CoMSIA methods.

    Science.gov (United States)

    Xu, A; Zhang, Y; Ran, T; Liu, H; Lu, S; Xu, J; Xiong, X; Jiang, Y; Lu, T; Chen, Y

    2015-01-01

    Bruton's tyrosine kinase (BTK) plays a crucial role in B-cell activation and development, and has emerged as a new molecular target for the treatment of autoimmune diseases and B-cell malignancies. In this study, two- and three-dimensional quantitative structure-activity relationship (2D and 3D-QSAR) analyses were performed on a series of pyridine and pyrimidine-based BTK inhibitors by means of genetic algorithm optimized multivariate adaptive regression spline (GA-MARS) and comparative molecular similarity index analysis (CoMSIA) methods. Here, we propose a modified MARS algorithm to develop 2D-QSAR models. The top ranked models showed satisfactory statistical results (2D-QSAR: Q(2) = 0.884, r(2) = 0.929, r(2)pred = 0.878; 3D-QSAR: q(2) = 0.616, r(2) = 0.987, r(2)pred = 0.905). Key descriptors selected by 2D-QSAR were in good agreement with the conclusions of 3D-QSAR, and the 3D-CoMSIA contour maps facilitated interpretation of the structure-activity relationship. A new molecular database was generated by molecular fragment replacement (MFR) and further evaluated with GA-MARS and CoMSIA prediction. Twenty-five pyridine and pyrimidine derivatives as novel potential BTK inhibitors were finally selected for further study. These results also demonstrated that our method can be a very efficient tool for the discovery of novel potent BTK inhibitors.

  2. Docking and QSAR Studies of Aryl-valproic Acid Derivatives to Identify Antiproliferative Agents Targeting the HDAC8.

    Science.gov (United States)

    Martínez-Pacheco, Heidy; Ramírez-Galicia, Guillermo; Vergara-Arias, Midalia; Gertsch, Jurg; Fragoso-Vazquez, Jonathan Manuel; Mendez-Luna, David; Abujamra, A L; Cristina, Cabrera-Perez Laura; Cecilia, Rosales-Hernandez Martha; Mendoza-Lujambio, I; Correa-Basurto, Jose

    2017-01-01

    Histone deacetylase 8 (HDAC8) is a plausible target for the development of novel anticancer drugs using a metal-chelating group and hydrophobic moieties as pharmacophores. It is known that valproic acid (administered as its salt, sodium valproate; VPANa+) is an HDAC8 inhibitor characterized by its hydrophobic chains. Nevertheless, VPA is hepatotoxic and VPA analogues might be explored for less hepatotoxic antiproliferative compounds. In this work, docking and QSAR studies of 500 aryl-VPA derivatives as possible HDAC8 inhibitors were performed in order to explore and select potential anti-proliferative compounds. Docking results identified π-π, hydrogen bonds as the most important noncovalent interactions between HDAC8 (PDB: 3F07) and the ligands tested, whereas Belm4 was the best QSAR descriptor and classified as a 2D-BCUT descriptor. Based on theoretical studies, compound DAVP042 was synthesized and evaluated in vitro for its antiproliferative activities on several cancer cell lines (A549-lung, MCF-7-breast, HCT116-colon and U937- lymphoid tissue) in comparison to VPA, as well as for its inhibitory activity on HDAC8 using in vitro models. DAVP042 demonstrated to have antiproliferative activity on all cancer cell lines employed, not only suggesting that this compound should be further studied, but also demonstrating that the methodology herein employed is appropriated to identify new therapeutic candidates. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  3. Molecular Modeling Studies of 11β-Hydroxysteroid Dehydrogenase Type 1 Inhibitors through Receptor-Based 3D-QSAR and Molecular Dynamics Simulations

    Directory of Open Access Journals (Sweden)

    Haiyan Qian

    2016-09-01

    Full Text Available 11β-Hydroxysteroid dehydrogenase type 1 (11β-HSD1 is a potential target for the treatment of numerous human disorders, such as diabetes, obesity, and metabolic syndrome. In this work, molecular modeling studies combining molecular docking, 3D-QSAR, MESP, MD simulations and free energy calculations were performed on pyridine amides and 1,2,4-triazolopyridines as 11β-HSD1 inhibitors to explore structure-activity relationships and structural requirement for the inhibitory activity. 3D-QSAR models, including CoMFA and CoMSIA, were developed from the conformations obtained by docking strategy. The derived pharmacophoric features were further supported by MESP and Mulliken charge analyses using density functional theory. In addition, MD simulations and free energy calculations were employed to determine the detailed binding process and to compare the binding modes of inhibitors with different bioactivities. The binding free energies calculated by MM/PBSA showed a good correlation with the experimental biological activities. Free energy analyses and per-residue energy decomposition indicated the van der Waals interaction would be the major driving force for the interactions between an inhibitor and 11β-HSD1. These unified results may provide that hydrogen bond interactions with Ser170 and Tyr183 are favorable for enhancing activity. Thr124, Ser170, Tyr177, Tyr183, Val227, and Val231 are the key amino acid residues in the binding pocket. The obtained results are expected to be valuable for the rational design of novel potent 11β-HSD1 inhibitors.

  4. A Combination of 3D-QSAR, Molecular Docking and Molecular Dynamics Simulation Studies of Benzimidazole-Quinolinone Derivatives as iNOS Inhibitors

    Directory of Open Access Journals (Sweden)

    Peixun Liu

    2012-09-01

    Full Text Available Inducible Nitric Oxide Synthase (iNOS has been involved in a variety of diseases, and thus it is interesting to discover and optimize new iNOS inhibitors. In previous studies, a series of benzimidazole-quinolinone derivatives with high inhibitory activity against human iNOS were discovered. In this work, three-dimensional quantitative structure-activity relationships (3D-QSAR, molecular docking and molecular dynamics (MD simulation approaches were applied to investigate the functionalities of active molecular interaction between these active ligands and iNOS. A QSAR model with R2 of 0.9356, Q2 of 0.8373 and Pearson-R value of 0.9406 was constructed, which presents a good predictive ability in both internal and external validation. Furthermore, a combined analysis incorporating the obtained model and the MD results indicates: (1 compounds with the proper-size hydrophobic substituents at position 3 in ring-C (R3 substituent, hydrophilic substituents near the X6 of ring-D and hydrophilic or H-bond acceptor groups at position 2 in ring-B show enhanced biological activities; (2 Met368, Trp366, Gly365, Tyr367, Phe363, Pro344, Gln257, Val346, Asn364, Met349, Thr370, Glu371 and Tyr485 are key amino acids in the active pocket, and activities of iNOS inhibitors are consistent with their capability to alter the position of these important residues, especially Glu371 and Thr370. The results provide a set of useful guidelines for the rational design of novel iNOS inhibitors.

  5. The Danish (Q)SAR Database Update Project

    DEFF Research Database (Denmark)

    Nikolov, Nikolai Georgiev; Dybdahl, Marianne; Abildgaard Rosenberg, Sine

    2013-01-01

    The Danish (Q)SAR Database is a collection of predictions from quantitative structure–activity relationship ((Q)SAR) models for over 70 environmental and human health-related endpoints (covering biodegradation, metabolism, allergy, irritation, endocrine disruption, teratogenicity, mutagenicity......, carcinogenicity and others), each of them available for 185,000 organic substances. The database has been available online since 2005 (http://qsar.food.dtu.dk). A major update project for the Danish (Q)SAR database is under way, with a new online release planned in the beginning of 2015. The updated version...

  6. Synthesis and QSAR Study of (4-Oxo-3-aryl-3,4-dihydro-quinazolin-2-ylsulfanyl-propionic Acid arylidene/aryl-ethylidene-hydrazides via Microwave Assisted Solvent Free Reations

    Directory of Open Access Journals (Sweden)

    M. B. Deshmukh

    2004-01-01

    Full Text Available In the present work, s-alkylated derivatives of thio-quinazolinone were obtained using Methyl 2-chloro propionate via a solvent-free microwave-assisted method. The alkylated thio quinazolinones were further sequentially condensed with hydrazine hydrate and different aromatic aldehydes to get the hydrazides, which were studied for QSAR. The synthesized compounds were subjected to a prediction of biological activities. A software application (PASS was used for this purpose. . The relationship between structure and different biological activities was studied and the different derivatives were recommended for the screening of some specific activities like anti-tuberculosic, anti-mycobacterial & HDL cholesterol increasing activities.

  7. 3D-QSAR and virtual screening studies of thiazolidine-2,4-dione analogs: Validation of experimental inhibitory potencies towards PIM-1 kinase

    Science.gov (United States)

    Asati, Vivek; Bharti, Sanjay Kumar; Budhwani, Ashok Kumar

    2017-04-01

    The proviral insertion site in moloney murine leukemia virus (PIM) is a family of serine/threonine kinase of Ca2+-calmodulin-dependent protein kinase (CAMK) group which is responsible for the activation and regulation of cellular transcription and translation. The three isoforms of PIM kinase (PIM-1, PIM-2 and PIM-3) share high homology and functional idleness are widely expressed and involved in a variety of biological processes including cell survival, proliferation, differentiation and apoptosis. Altered expression of PIM-1 kinase correlated with hematologic malignancies and solid tumors. In the present study, atom-based 3D-QSAR, docking and virtual screening studies have been performed on a series of thiazolidine-2,4-dione derivatives as PIM-1 kinase inhibitors. 3D-QSAR and docking approach has shortlisted the most active thiazolidine-2,4-dione derivatives such as 28, 31, 33 and 35 with the incorporation of more than one structural feature in a single molecule. External validations by various parameters and molecular docking studies at the active site of PIM-1 kinase have proved the reliability of the developed 3D-QSAR model. The generated pharmacophore (AADHR.33) from 3D-QSAR study was used for screening of drug like compounds from ZINC database, where ZINC15056464 and ZINC83292944 showed potential binding affinities at the active site amino acid residues (LYS67, GLU171, ASP128 and ASP186) of PIM-1 kinase (PDB ID: "pdb:4DTK").

  8. Estudos de QSAR 3D para um conjunto de inibidores de butirilcolinesterase humana QSAR 3D studies of a series of human butyrylcholinesterase inhibitors

    Directory of Open Access Journals (Sweden)

    Humberto F. Freitas

    2009-01-01

    Full Text Available Alzheimer's disease (AD is considered the main cause of cognitive decline in adults. The available therapies for AD treatment seek to maintain the activity of cholinergic system through the inhibition of the enzyme acetylcholinesterase. However, butyrylcholinesterase (BuChE can be considered an alternative target for AD treatment. Aiming at developing new BuChE inhibitors, robust QSAR 3D models with high predictive power were developed. The best model presents a good fit (r²=0.82, q²=0.76, with two PCs and high predictive power (r²predict=0.88. Analysis of regression vector shows that steric properties have considerable importance to the inhibition of the BuChE.

  9. 3D-QSAR study indicates an enhancing effect of membrane ions on psychiatric drugs targeting serotonin receptor 5-HT1A.

    Science.gov (United States)

    Avram, Speranţa; Milac, Adina-Luminiţa; Mihailescu, Dan

    2012-04-01

    Antidepressants and antipsychotics are psychiatric agents used for the treatment of various types of psychiatric diseases. Although currently among the most commonly prescribed drugs, their effectiveness and adverse effects are the topic of many studies and controversial claims. Here we generate QSAR models based on compounds series including 20 drugs recommended for two critical psychiatric diseases: depression and schizophrenia and we use these QSAR models to predict the biological activity of these 20 antidepressants and antipsychotics. We establish the membrane ions' contributions (sodium, potassium, calcium and iron) mediated by water to the antagonism of these drugs at the 5-HT1A receptor. The reliability of our QSAR models in predicting compounds activity is indicated by significant values for cross-validated correlation q² (0.60-0.76) and fitted correlation r² (0.96-0.98) coefficients. Our results indicate that potassium, calcium and iron play a key role for the antagonistic activity of drugs at the 5-HT1A receptor. Moreover, based on the established QSAR equations, we analysed 24 new escitalopram derivatives as possibly improved antidepressants targeting the 5-HT1A receptor. We identified that the presence of methyl groups and hydrogen atoms improves antidepressant activity while the simultaneous presence of ethyl, propyl or halogens decreased drastically antidepressant activity at the 5-HT1A site.

  10. Synthesis, screening and QSAR studies of 3-benzoyl-2-oxo/thioxo-1 ...

    African Journals Online (AJOL)

    A quantitative structure activity relationship study was made using various descriptors. Several statistical expressions were developed using stepwise multiple linear regression analysis. The best quantitative structure activity relationship model was further cross validated. The study revealed that total positive partial charge ...

  11. Performance of Deep and Shallow Neural Networks, the Universal Approximation Theorem, Activity Cliffs, and QSAR.

    Science.gov (United States)

    Winkler, David A; Le, Tu C

    2017-01-01

    Neural networks have generated valuable Quantitative Structure-Activity/Property Relationships (QSAR/QSPR) models for a wide variety of small molecules and materials properties. They have grown in sophistication and many of their initial problems have been overcome by modern mathematical techniques. QSAR studies have almost always used so-called "shallow" neural networks in which there is a single hidden layer between the input and output layers. Recently, a new and potentially paradigm-shifting type of neural network based on Deep Learning has appeared. Deep learning methods have generated impressive improvements in image and voice recognition, and are now being applied to QSAR and QSAR modelling. This paper describes the differences in approach between deep and shallow neural networks, compares their abilities to predict the properties of test sets for 15 large drug data sets (the kaggle set), discusses the results in terms of the Universal Approximation theorem for neural networks, and describes how DNN may ameliorate or remove troublesome "activity cliffs" in QSAR data sets. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  12. Synthesis, antimicrobial, anticancer evaluation and QSAR studies of 2/3-bromo-N′-(substituted benzylidene/3-phenylallylidenebenzohydrazides

    Directory of Open Access Journals (Sweden)

    Pradeep Kumar

    2017-05-01

    Full Text Available In the present study, a series of 2/3-bromo-N′-(substituted benzylidene/3-phenylallylidenebenzohydrazides was synthesized and evaluated in vitro for its antimicrobial and anticancer potentials. The results of antimicrobial and anticancer study indicated that compounds 3, 15 and 18 (pMICam = 1.62 μM/ml were found to be most potent antimicrobial agents and compound 4 (IC50 = 1.88 ± 0.03 μM was found to be the most potent anticancer agent. The results of QSAR analysis indicated the importance of topological parameters, Balaban index (J and valence first and second order molecular connectivity indices (1χv and 2χv in describing antimicrobial activity of the synthesized benzohydrazides.

  13. Bee algorithm and adaptive neuro-fuzzy inference system as tools for QSAR study toxicity of substituted benzenes to Tetrahymena pyriformis.

    Science.gov (United States)

    Zarei, Kobra; Atabati, Morteza; Kor, Kamalodin

    2014-06-01

    A quantitative structure-activity relationship (QSAR) was developed to predict the toxicity of substituted benzenes to Tetrahymena pyriformis. A set of 1,497 zero- to three-dimensional descriptors were used for each molecule in the data set. A major problem of QSAR is the high dimensionality of the descriptor space; therefore, descriptor selection is one of the most important steps. In this paper, bee algorithm was used to select the best descriptors. Three descriptors were selected and used as inputs for adaptive neuro-fuzzy inference system (ANFIS). Then the model was corrected for unstable compounds (the compounds that can be ionized in the aqueous solutions or can easily metabolize under some conditions). Finally squared correlation coefficients were obtained as 0.8769, 0.8649 and 0.8301 for training, test and validation sets, respectively. The results showed bee-ANFIS can be used as a powerful model for prediction of toxicity of substituted benzenes to T. pyriformis.

  14. Advances in quantitative structure-activity relationship models of anti-Alzheimer's agents.

    Science.gov (United States)

    Ambure, Pravin; Roy, Kunal

    2014-06-01

    Alzheimer's disease (AD) is one of the lethal diseases, mainly affecting older people. The unclear root cause and involvement of various enzymes in the pathological conditions confirm the complexity of the disease. Quantitative structure-activity relationship (QSAR) techniques are of great significance in the design of drugs against AD. In the present review, the authors provide a basic background about AD and QSAR techniques. Furthermore, they review the various QSAR studies reported against various targets of AD. The information provided for each QSAR study includes chemical scaffold and target enzyme under study, applied QSAR technique and outcomes of the respective study. In silico techniques like QSAR hold great potential in designing leads against a complex disease like AD. In combination with other in silico techniques, QSAR can provide more useful and rational insight to facilitate the discovery of novel compounds. Only few QSAR studies on imaging agents have been reported; hence, more QSAR studies are recommended to explore the biomarker or imaging agents for improving diagnosis. Again, for proper symptomatic treatment, multi-target drugs acting on more than one target are required. Hence, more multi-target QSAR studies are recommended in future to achieve this goal.

  15. Antibacterial Activity of Some 3-(Arylideneamino-2-phenylquinazoline-4(3H-ones: Synthesis and Preliminary QSAR Studies

    Directory of Open Access Journals (Sweden)

    Ranadhir Chakraborty

    2007-10-01

    Full Text Available Synthesis of ten 3-(arylideneamino-2-phenylquinazoline-4(3H-ones is reported. All the compounds contained a common phenyl group at the 2-position, while the substituents on the arylideneamino group were varied. The compounds were investigated for their antimicrobial activity against both Gram-positive (Staphylococcus aureus 6571 and Bacillus subtilis and Gram-negative bacteria (Escherichia coli K12 and Shigella dysenteriae 6 using a turbidometric assay method. It was found that the incorporation of the 3-arylideneamino substituent enhanced the anti-bacterial activity of the quinazolone system. The preliminary QSAR studies were done using some computer derived property descriptors, calculated values of partition coefficients as well as usual Hammett’s sigma constants and the substituent’s molar refractivity.

  16. Synthesis, in vitro antimicrobial, anticancer evaluation and QSAR studies of N′-(substituted-4-(butan-2-lideneaminobenzohydrazides

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    Mahak Saini

    2014-09-01

    Full Text Available A series of N′-(substituted-4-(butan-2-ylideneaminobenzohydrazides (1–21 was synthesized and characterized by physicochemical as well as spectral means. The synthesized compounds were screened for their in vitro antimicrobial and anticancer potentials. The synthesized compounds displayed higher antifungal potential as compared to antibacterial potential. Besides having good antifungal potential, the synthesized compounds were having appreciable anticancer potential and a number of compounds displayed higher anticancer potential than the standard drug, carboplatin. The results of QSAR studies demonstrated the importance of steric parameter, molar refractivity (MR, topological parameters, third order molecular connectivity index (3χ, Kier’s first order shape index (κ1 in describing the antimicrobial activity of N′-(substituted-4-(butan-2-ylideneaminobenzohydrazides.

  17. Quantitative structure-activity relationships (QSAR) of 4-amino-2,6-diarylpyrimidine-5-carbonitriles with anti-inflammatory activity

    Energy Technology Data Exchange (ETDEWEB)

    Silva, Joao Bosco P. da; Ramos, Mozart N.; Barros Neto, Benicio de [Universidade Federal de Pernambuco (UFPE), Recife, PE (Brazil). Dept. de Quimica Fundamental]. E-mail: mramos@ufpe.br; Melo, Sebastiao Jose de [Universidade Federal de Pernambuco (UFPE), Recife, PE (Brazil). Dept. de Antibioticos]. E-mail: melosebastiao@yahoo.com.br; Falcao, Emerson Peter da Silva [Universidade Federal de Pernambuco (UFPE), Recife, PE (Brazil). Centro Academico de Vitoria de Santo Antao; Catanho, Maria Teresa J. de Almeida [Universidade Federal de Pernambuco (UFPE), Recife, PE (Brazil). Dept. de Biofisica e Radiobiologia

    2008-07-01

    The experimental anti-inflammatory activities of eight 4-amino-2,6-diarylpyrimidine-5- carbonitriles were subjected to a QSAR analysis based on results from B3LYP/6-31G(d,p) and AM1 electronic structure calculations. Principal component analyses and regressions based on these data indicate that potentially more active compounds should have low dipole moment and partition coefficient values and also be affected by the values of the charges of the carbon atoms through which the two aromatic rings are bonded to the pyrimidinic ring. Two new molecules were predicted to be at least as active as those with the highest activities used in the model building stage. One of them, having a methoxy group attached to one of the aromatic rings, was predicted to have an anti-inflammatory activity value of 52.3%. This molecule was synthesized and its experimental activity was found to be 52.8%, in agreement with the AM1 theoretical prediction. This value is 5% higher than the largest value used for modeling. (author)

  18. QSAR studies on chalcones and flavonoids as anti-tuberculosis agents using genetic function approximation (GFA) method.

    Science.gov (United States)

    Sivakumar, Ponnurengam Malliappan; Geetha Babu, Sethu Kailasam; Mukesh, Doble

    2007-01-01

    Design of compounds having good anti-tubercular activity is gaining much importance in the field of tuberculosis research due to reemergence of antibiotic resistance strains. In this paper quantitative structure activity relationships (QSAR) were developed on chalcones, chalcone-like compounds, flavones and flavanones to understand the relationship between biological activity and structural features. Genetic function approximation (GFA) method was used to identify the descriptors that would lead to good regression equations. The best molecular descriptors identified were Jurs descriptors (Jurs charged partial surface area), hydrogen bond donor, principal moment of inertia, molecular energy, dipole magnetic, molecular area, absorption, distribution, metabolism and excretion (ADME) properties and Chi indices (Kier & Hall chi connectivity indices). Excellent statistically significant models were developed by this approach (r(2)=0.8-0.97) for the four groups of compounds. The cross validated r(2) (XV r(2)) which is an indication of the predictive capability of the model for all the cases was also very good (=0.79-0.94).

  19. Synthesis and QSAR Study of Novel 6-Chloro-3-(2-Arylmethylene-1-methylhydrazino-1,4,2-benzodithiazine 1,1-Dioxide Derivatives with Anticancer Activity

    Directory of Open Access Journals (Sweden)

    Jarosław Sławiński

    2015-04-01

    Full Text Available A series of new 6-chloro-3-(2-arylmethylene-1-methylhydrazino-1,4,2-benzodithiazine 1,1-dioxide derivatives were effectively synthesized from N-methyl-N-(6-chloro-1,1-dioxo-1,4,2-benzodithiazin-3-ylhydrazines. The intermediate compounds as well as the products, were evaluated for their cytotoxic effects toward three human cancer cell lines. All compounds shown moderate or weak cytotoxic effects against the tested cancer cell lines, but selective cytotoxic effects were observed. Compound 16 exhibited the most potent cytotoxic activity against the HeLa cell line, with an IC50 value of 10 µM, while 14 was the most active against the MCF-7 and HCT-116 cell lines, affording IC50 values of 15 µM and 16 µM, respectively. The structure-activity relationship was evaluated based on QSAR methodology. The QSAR MCF-7 model indicated that natural charge on carbon atom C13 and energy of highest occupied molecular orbital (HOMO are highly involved in cytotoxic activity against MCF-7 cell line. The cytotoxic activity of compounds against HCT-116 cell line is dependent on natural charge on carbon atom C13 and electrostatic charge on nitrogen atom N10. The obtained QSAR models could provide guidelines for further development of novel anticancer agents.

  20. DFT/PCM, QTAIM, 1H NMR conformational studies and QSAR modeling of thirty-two anti-Leishmania amazonensis Morita-Baylis-Hillman Adducts

    Science.gov (United States)

    Filho, Edilson B. A.; Moraes, Ingrid A.; Weber, Karen C.; Rocha, Gerd B.; Vasconcellos, Mário L. A. A.

    2012-08-01

    Morita-Baylis-Hillman Adducts (MBHA) has been recently synthesized and bio-evaluated by our research group against Leishmania amazonensis, parasite that causes cutaneous and mucocutaneous leishmaniasis. We present here a theoretical conformational study of thirty-two leismanicidal MBHA by B3LYP/6-31+g(d) calculations with Polarized Continuum Model (PCM) to simulate water influence. Intramolecular Hydrogen Bonds (IHBs) indicated to control the most conformational preferences of MBHA. Quantum Theory Atoms in Molecules (QTAIM) calculations were able to characterize these interactions at Bond Critical Point level. Compounds presenting an unusual seven member IHB between NO2 group and hydroxyl moiety, supported by experimental spectroscopic data, showed a considerable improvement of biological activity (lower IC50 values). These results are in accordance to redox NO2 mechanism of action. Based on structural observations, some molecular descriptors were calculated and submitted to Quantitative Structure-Activity Relationship (QSAR) studies through the PLS Regression Method. These studies provided a model with good validation parameters values (R2 = 0.71, Q2 = 0.61 and Qext2 = 0.92).

  1. Combretastatin A-4 based thiophene derivatives as antitumor agent: Development of structure activity correlation model using 3D-QSAR, pharmacophore and docking studies

    Directory of Open Access Journals (Sweden)

    Vijay K. Patel

    2017-12-01

    Full Text Available The structure and ligand based synergistic approach is being applied to design ligands more correctly. The present report discloses the combination of structure and ligand based tactics i.e., molecular docking, energetic based pharmacophore, pharmacophore and atom based 3D-QSAR modeling for the analysis of thiophene derivatives as anticancer agent. The main purpose of using structure and ligand based synergistic approach is to ascertain a correlation between structure and its biological activity. Thiophene derivatives have been found to possess cytotoxic activity in several cancer cell lines and its mechanism of action basically involves the binding to the colchicine site on β-tubulin. The structure based approach (molecular docking was performed on a series of thiophene derivatives. All the structures were docked to colchicine binding site of β tubulin for examining the binding affinity of compounds for antitumor activity. The pharmacophore and atom based 3D-QSAR modeling was accomplished on a series of thiophene (32 compounds analogues. Five-point common pharmacophore hypotheses (AAAAR.38 were selected for alignment of all compounds. The atom based 3D-QSAR models were developed by selection of 23 compounds as training set and 9 compounds as test set, demonstrated good partial least squares statistical results. The generated common pharmacophore hypothesis and 3D-QSAR models were validated further externally by measuring the activity of database compounds and assessing it with actual activity. The common pharmacophore hypothesis AAAAR.38 resulted in a 3D-QSAR model with excellent PLSs data for factor two characterized by the best predication coefficient Q2 (cross validated r2 (0.7213, regression R2 (0.8311, SD (0.3672, F (49.2, P (1.89E-08, RMSE (0.3864, Stability (0.8702, Pearson-r (0.8722. The results of these molecular modeling studies i.e., molecular docking, energetic based pharmacophore, pharmacophore and atom based 3D-QSAR modeling

  2. QSAR study of malonyl-CoA decarboxylase inhibitors using GA-MLR and a new strategy of consensus modeling.

    Science.gov (United States)

    Li, Jiazhong; Lei, Beilei; Liu, Huanxiang; Li, Shuyan; Yao, Xiaojun; Liu, Mancang; Gramatica, Paola

    2008-12-01

    Quantitative structure-activity relationship (QSAR) of a series of structural diverse malonyl-CoA decarboxylase (MCD) inhibitors have been investigated by using the predictive single model as well as the consensus analysis based on a new strategy proposed by us. Self-organizing map (SOM) neural network was employed to divide the whole data set into representative training set and test set. Then a multiple linear regressions (MLR) model population was built based on the theoretical molecular descriptors selected by Genetic Algorithm using the training set. In order to analyze the diversity of these models, multidimensional scaling (MDS) was employed to explore the model space based on the Hamming distance matrix calculated from each two models. In this space, Q(2) (cross-validated R(2)) guided model selection (QGMS) strategy was performed to select submodels. Then consensus modeling was built by two strategies, average consensus model (ACM) and weighted consensus model (WCM), where each submodel had a different weight according to the contribution of model expressed by MLR regression coefficients. The obtained results prove that QGMS is a reliable and practical method to guide the submodel selection in consensus modeling building and our weighted consensus model (WCM) strategy is superior to the simple ACM. 2008 Wiley Periodicals, Inc.

  3. 3D-QSAR and docking studies on 1-hydroxypyridin-2-one compounds as mutant isocitrate dehydrogenase 1 inhibitors

    Science.gov (United States)

    Wang, Zhenya; Chang, Yiqun; Han, Yushui; Liu, Kangjia; Hou, Jinsong; Dai, Chengli; Zhai, Yuanhao; Guo, Jialiang; Sun, Pinghua; Lin, Jing; Chen, Weimin

    2016-11-01

    Mutation of isocitrate dehydrogenase 1 (IDH1) which is frequently found in certain cancers such as glioma, sarcoma and acute myeloid leukemia, has been proven to be a potent drug target for cancer therapy. In silico methodologies such as 3D-QSAR and molecular docking were performed to explore compounds with better mutant isocitrate dehydrogenase 1 (MIDH1) inhibitory activity using a series of 40 newly reported 1-hydroxypyridin-2-one compounds as MIDH1 inhibitors. The satisfactory CoMFA and CoMSIA models obtained after internal and external cross-validation gave q2 values of 0.691 and 0.535, r2 values of 0.984 and 0.936, respectively. 3D contour maps generated from CoMFA and CoMSIA along with the docking results provided information about the structural requirements for better MIDH1 inhibitory activity. Based on the structure-activity relationship, 17 new potent molecules with better predicted activity than the most active compound in the literature have been designed.

  4. QSAR studies of the bioactivity of hepatitis C virus (HCV) NS3/4A protease inhibitors by multiple linear regression (MLR) and support vector machine (SVM).

    Science.gov (United States)

    Qin, Zijian; Wang, Maolin; Yan, Aixia

    2017-07-01

    In this study, quantitative structure-activity relationship (QSAR) models using various descriptor sets and training/test set selection methods were explored to predict the bioactivity of hepatitis C virus (HCV) NS3/4A protease inhibitors by using a multiple linear regression (MLR) and a support vector machine (SVM) method. 512 HCV NS3/4A protease inhibitors and their IC 50 values which were determined by the same FRET assay were collected from the reported literature to build a dataset. All the inhibitors were represented with selected nine global and 12 2D property-weighted autocorrelation descriptors calculated from the program CORINA Symphony. The dataset was divided into a training set and a test set by a random and a Kohonen's self-organizing map (SOM) method. The correlation coefficients (r 2 ) of training sets and test sets were 0.75 and 0.72 for the best MLR model, 0.87 and 0.85 for the best SVM model, respectively. In addition, a series of sub-dataset models were also developed. The performances of all the best sub-dataset models were better than those of the whole dataset models. We believe that the combination of the best sub- and whole dataset SVM models can be used as reliable lead designing tools for new NS3/4A protease inhibitors scaffolds in a drug discovery pipeline. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. Design, synthesis and QSAR study of novel isatin analogues inspired Michael acceptor as potential anticancer compounds.

    Science.gov (United States)

    Wang, Jiabing; Yun, Di; Yao, Jiali; Fu, Weitao; Huang, Fangyan; Chen, Liping; Wei, Tao; Yu, Cuijuan; Xu, Haineng; Zhou, Xiaoou; Huang, Yanqing; Wu, Jianzhang; Qiu, Peihong; Li, Wulan

    2017-12-18

    Molecular hybridization is considered as an effective tactic to develop drugs for the treatment of cancer. A series of novel hybrid compounds of isatin and Michael acceptor were designed and synthesized on the basis of association principle. These hybrid compounds were tested for cytotoxic potential against human cancer cell lines namely, BGC-823, SGC-7901 and NCI-H460 by MTT assay. Most compounds showed good anti-growth activities in all tested human cancer cells. SAR and QSAR analysis may provide vital information for the future development of novel anti-cancer inhibitors. Notably, compound 6a showed potent growth inhibition on BGC-823, SGC-7901 and NCI-H460 with the IC50 values of 3.6 ± 0.6, 5.7 ± 1.2, 3.2 ± 0.7 μM, respectively. Besides, colony formation assays, wound healing assays and flow cytometry analysis indicated 6a exhibited a potent anti-growth and anti-migration ability in a concentration-dependence manner through arrested cells in the G2/M phase of cell cycle. Moreover, 6a significantly repressed tumor growth in a NCI-H460 xenograft mouse model. Overall, our findings suggested isatin analogues inspired Michael acceptor may provide promising lead compounds for the development of cancer chemotherapeutics. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

  6. A combination of pharmacophore modeling, atom-based 3D-QSAR, molecular docking and molecular dynamics simulation studies on PDE4 enzyme inhibitors.

    Science.gov (United States)

    Tripuraneni, Naga Srinivas; Azam, Mohammed Afzal

    2016-11-01

    Phosphodiesterases 4 enzyme is an attractive target for the design of anti-inflammatory and bronchodilator agents. In the present study, pharmacophore and atom-based 3D-QSAR studies were carried out for pyrazolopyridine and quinoline derivatives using Schrödinger suite 2014-3. A four-point pharmacophore model was developed using 74 molecules having pIC50 ranging from 10.1 to 4.5. The best four feature model consists of one hydrogen bond acceptor, two aromatic rings, and one hydrophobic group. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a high correlation coefficient (R(2 )= .9949), cross validation coefficient (Q(2 )= .7291), and Pearson-r (.9107) at six component partial least square factor. The external validation indicated that our QSAR model possessed high predictive power with R(2) value of .88. The generated model was further validated by enrichment studies using the decoy test. Molecular docking, free energy calculation, and molecular dynamics (MD) simulation studies have been performed to explore the putative binding modes of these ligands. A 10-ns MD simulation confirmed the docking results of both stability of the 1XMU-ligand complex and the presumed active conformation. Outcomes of the present study provide insight in designing novel molecules with better PDE4 inhibitory activity.

  7. 3D-QSAR (CoMFA, CoMSIA), molecular docking and molecular dynamics simulations study of 6-aryl-5-cyano-pyrimidine derivatives to explore the structure requirements of LSD1 inhibitors.

    Science.gov (United States)

    Ding, Lina; Wang, Zhi-Zheng; Sun, Xu-Dong; Yang, Jing; Ma, Chao-Ya; Li, Wen; Liu, Hong-Min

    2017-08-01

    Recently, Histone Lysine Specific Demethylase 1 (LSD1) was regarded as a promising anticancer target for the novel drug discovery. And several small molecules as LSD1 inhibitors in different structures have been reported. In this work, we carried out a molecular modeling study on the 6-aryl-5-cyano-pyrimidine fragment LSD1 inhibitors using three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking and molecular dynamics simulations. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used to generate 3D-QSAR models. The results show that the best CoMFA model has q(2)=0.802, r(2)ncv=0.979, and the best CoMSIA model has q(2)=0.799, r(2)ncv=0.982. The electrostatic, hydrophobic and H-bond donor fields play important roles in the models. Molecular docking studies predict the binding mode and the interactions between the ligand and the receptor protein. Molecular dynamics simulations results reveal that the complex of the ligand and the receptor protein are stable at 300K. All the results can provide us more useful information for our further drug design. Copyright © 2017. Published by Elsevier Ltd.

  8. Ranking of aquatic toxicity of esters modelled by QSAR.

    Science.gov (United States)

    Papa, Ester; Battaini, Francesca; Gramatica, Paola

    2005-02-01

    Alternative methods like predictions based on Quantitative Structure-Activity Relationships (QSARs) are now accepted to fill data gaps and define priority lists for more expensive and time consuming assessments. A heterogeneous data set of 74 esters was studied for their aquatic toxicity, and available experimental toxicity data on algae, Daphnia and fish were used to develop statistically validated QSAR models, obtained using multiple linear regression (MLR) by the OLS (Ordinary Least Squares) method and GA-VSS (Variable Subset Selection by Genetic Algorithms) to predict missing values. An ESter Aquatic Toxicity INdex (ESATIN) was then obtained by combining, by PCA, experimental and predicted toxicity data, from which model outliers and esters highly influential due to their structure had been eliminated. Finally this integrated aquatic toxicity index, defined by the PC1 score, was modelled using only a few theoretical molecular descriptors. This last QSAR model, statistically validated for its predictive power, could be proposed as a preliminary evaluative method for screening/prioritising esters according to their integrated aquatic toxicity, just starting from their molecular structure.

  9. QSAR studies of antimicrobial activity of 1,3-disubstituted-1H-naphtho[1,2-e][1,3]oxazines using topological descriptors

    Directory of Open Access Journals (Sweden)

    Vikas Verma

    2017-02-01

    Full Text Available The antimicrobial activity of 1,3-disubstituted-1H-naphtho[1,2-e][1,3]oxazines was correlated with their physicochemical parameters using Hansch analysis for first time. The QSAR models were developed by both linear and multiple linear regression and the developed models were cross validated by the “leave one out” technique. The QSAR studies indicated that the antibacterial activity of synthesized compounds was governed by topological parameters, Balaban index (J, Kier's second order molecular index (κα2 and third order molecular connectivity index (3χ and the antifungal activity was governed by valance first order molecular connectivity index (1χv. The practical applicability of developed models was explored by the design of new compounds based on the information derived from the developed equations.

  10. A Quantitative Structure-Activity Relationship and Molecular Modeling Study on a Series of Heteroaryl- and Heterocyclyl-Substituted Imidazo[1,2-a]Pyridine Derivatives Acting as Acid Pump Antagonists

    OpenAIRE

    Neeraj Agarwal; Anubha Bajpai; Gupta, Satya P

    2013-01-01

    A quantitative structure-activity relationship (QSAR) and molecular docking study has been performed on a series of heteroaryl- and heterocyclyl-substituted imidazo[1,2-a]pyridine derivatives acting as acid pump antagonists in order to have a better understanding of the mechanism of H+/K+-ATPase inhibition. The QSAR study shows a significant correlation of activity with Global Topological Charge Indices (GTCI) of the compounds and the hydrophobic constant ? of some substituents, indicating th...

  11. Pharmacophore modelling and atom-based 3D-QSAR studies on N-methyl pyrimidones as HIV-1 integrase inhibitors.

    Science.gov (United States)

    Reddy, Karnati Konda; Singh, Sanjeev Kumar; Dessalew, Nigus; Tripathi, Sunil Kumar; Selvaraj, Chandrabose

    2012-06-01

    Pharmacophore modelling and atom-based 3D-QSAR studies were carried out for a series of compounds belonging to N-methyl pyrimidones as HIV-1 integrase inhibitors. Based on the ligand-based pharmacophore model, we got 5-point pharmacophore model AADDR, with two hydrogen bond acceptors (A), two hydrogen bond donors (D) and one aromatic ring (R). The generated pharmacophore-based alignment was used to derive a predictive atom-based 3D-QSAR model for the training set (r(2) = 0.92, SD = 0.16, F = 84.8, N = 40) and for test set (Q(2) = 0.71, RMSE = 0.06, Pearson R = 0.90, N = 10). From these results, AADDR pharmacophore feature was selected as best common pharmacophore hypothesis, and atom-based 3D-QSAR results also support the outcome by means of favourable and unfavourable regions of hydrophobic and electron-withdrawing groups for the most potent compound 30. These results can be useful for further design of new and potent HIV-1 IN inhibitors.

  12. QSARs in ecotoxicological risk assessment

    NARCIS (Netherlands)

    Roode, De D.; Hoekzema, C.C.; Vries-Buitenweg, de S.; Waart, van de B.; Hoeven, Van der J.

    2006-01-01

    The need for more ecotoxicological data encourages the use of QSARs because of the reduction of (animal) testing, time and cost. QSARs may however only be used if they prove to be reliable and accurate. In this paper, four QSARs were attempted to predict toxicity for 170 compounds from a broad

  13. Pharmacophore Modelling and 3D-QSAR Studies on N(3)-Phenylpyrazinones as Corticotropin-Releasing Factor 1 Receptor Antagonists.

    Science.gov (United States)

    Kaur, Paramjit; Sharma, Vikas; Kumar, Vipin

    2012-01-01

    Pharmacophore modelling-based virtual screening of compound is a ligand-based approach and is useful when the 3D structure of target is not available but a few known active compounds are known. Pharmacophore mapping studies were undertaken for a set of 50 N(3)-phenylpyrazinones possessing Corticotropin-releasing Factor 1 (CRF 1) antagonistic activity. Six point pharmacophores with two hydrogen bond acceptors, one hydrogen bond donor, two hydrophobic regions, and one aromatic ring as pharmacophoric features were developed. Amongst them the pharmacophore hypothesis AADHHR.47 yielded a statistically significant 3D-QSAR model with 0.803 as R (2) value and was considered to be the best pharmacophore hypothesis. The developed pharmacophore model was externally validated by predicting the activity of test set molecules. The squared predictive correlation coefficient of 0.91 was observed between experimental and predicted activity values of test set molecules. The geometry and features of pharmacophore were expected to be useful for the design of selective CRF 1 receptor antagonists.

  14. QSAR analysis for some β-carboline derivatives as anti-tumor

    Directory of Open Access Journals (Sweden)

    Ravindra Kumar Chourasiya

    2016-09-01

    Full Text Available β-Carboline moieties are important structural subunits which occur as components of many biologically interesting molecules for antitumor activity. Quantitative structure–activity relationship (QSAR studies have been performed on β-carboline derivatives to explore the structural necessities for antitumor activity. 3D QSAR studies were done using V-Life Sciences MDS 3.0 drug designing module to explain the structural requirements for the anti-tumor activity. The 3D-QSAR was performed using the Step Wise K Nearest Neighbour Molecular Field Analysis [(SW kNN MFA] technique with the partial least-square (PLS method on a database. Obtained best 3D-QSAR model having high predictive ability with q2 = 0.743, r2 = 0.721, pred_r2 = 0.708 and standard error = 0.346, explaining the majority of the variance in the data with partial least square (PLS components. The results of the present study may be useful on the designing of more potent compounds as antitumor drugs.

  15. 2D-QSAR study, molecular docking, and molecular dynamics simulation studies of interaction mechanism between inhibitors and transforming growth factor-beta receptor I (ALK5).

    Science.gov (United States)

    Jiang, Meng-Nan; Zhou, Xiao-Ping; Sun, Dong-Ru; Gao, Huan; Zheng, Qing-Chuan; Zhang, Hong-Xing; Liang, Di

    2017-11-06

    Transforming growth factor type 1 receptor (ALK5) is kinase associated with a wide variety of pathological processes, and inhibition of ALK5 is a good strategy to treat many kinds of cancer and fibrotic diseases. Recently, a series of compounds have been synthesized as ALK5 inhibitors. However, the study of their selectivity against other potential targets remains elusive. In this research, a data-set of ALK5 inhibitors were collected and studied based on the combination of 2D-QSAR, molecular docking and molecular dynamics simulation. The quality of QSAR models were assessed statistically by F, R2, and R2ADJ, proved to be credible. The cross-validations for the models (q2LOO = 0.571 and 0.629, respectively) showed their robustness, while the external validations (r2test = 0.703 and 0.764, respectively) showed their predictive power. Besides, the predicted binding free energy results calculated by MM/GBSA method were in accordance with the experimental data, and the van der Waals energy term was the factor that had the most significant impact on ligand binding. What is more, several important residues were found to significantly affect the binding affinity. Finally, based on our analyses above, a proposed series of molecules were designed.

  16. Porphyrins as Corrosion Inhibitors for N80 Steel in 3.5% NaCl Solution: Electrochemical, Quantum Chemical, QSAR and Monte Carlo Simulations Studies.

    Science.gov (United States)

    Singh, Ambrish; Lin, Yuanhua; Quraishi, Mumtaz A; Olasunkanmi, Lukman O; Fayemi, Omolola E; Sasikumar, Yesudass; Ramaganthan, Baskar; Bahadur, Indra; Obot, Ime B; Adekunle, Abolanle S; Kabanda, Mwadham M; Ebenso, Eno E

    2015-08-18

    The inhibition of the corrosion of N80 steel in 3.5 wt. % NaCl solution saturated with CO2 by four porphyrins, namely 5,10,15,20-tetrakis(4-hydroxyphenyl)-21H,23H-porphyrin (HPTB), 5,10,15,20-tetra(4-pyridyl)-21H,23H-porphyrin (T4PP), 4,4',4″,4‴-(porphyrin-5,10,15,20-tetrayl)tetrakis(benzoic acid) (THP) and 5,10,15,20-tetraphenyl-21H,23H-porphyrin (TPP) was studied using electrochemical impedance spectroscopy (EIS), potentiodynamic polarization, scanning electrochemical microscopy (SECM) and scanning electron microscopy (SEM) techniques. The results showed that the inhibition efficiency, η% increases with increasing concentration of the inhibitors. The EIS results revealed that the N80 steel surface with adsorbed porphyrins exhibited non-ideal capacitive behaviour with reduced charge transfer activity. Potentiodynamic polarization measurements indicated that the studied porphyrins acted as mixed type inhibitors. The SECM results confirmed the adsorption of the porphyrins on N80 steel thereby forming a relatively insulated surface. The SEM also confirmed the formation of protective films of the porphyrins on N80 steel surface thereby protecting the surface from direct acid attack. Quantum chemical calculations, quantitative structure activity relationship (QSAR) were also carried out on the studied porphyrins and the results showed that the corrosion inhibition performances of the porphyrins could be related to their EHOMO, ELUMO, ω, and μ values. Monte Carlo simulation studies showed that THP has the highest adsorption energy, while T4PP has the least adsorption energy in agreement with the values of σ from quantum chemical calculations.

  17. Structure Modification toward Applicability Domain of a QSAR/QSPR Model Considering Activity/Property.

    Science.gov (United States)

    Ochi, Shoki; Miyao, Tomoyuki; Funatsu, Kimito

    2017-12-01

    In drug and material design, the activity and property values of the designed chemical structures can be predicted by quantitative structure-activity and structure-property relationship (QSAR/QSPR) models. When a QSAR/QSPR model is applied to chemical structures, its applicability domain (AD) must be considered. The predicted activity/property values are only reliable for chemical structures inside the AD. Chemical structures outside the AD are usually neglected, as the predicted values are unreliable. The purpose of this study is to develop a methodology for obtaining novel chemical structures with the desired activity or property based on a QSAR/QSPR model by making use of the neglected structures. We propose a structure modification strategy for the AD that considers the activity and property simultaneously. The AD is defined by a one-class support vector machine and the structure modification is guided by a partial derivative of the AD model and matched molecular pairs analysis. Three proof-of-concept case studies generate novel chemical structures inside the AD that exhibit preferable activity/property values according to the QSAR/QSPR model. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  18. Quantitative structure-activity relationship study of phloroglucinol-terpene adducts as anti-leishmanial agents.

    Science.gov (United States)

    Bharate, Sandip B; Singh, Inder Pal

    2011-07-15

    Phloroglucinol class of natural products occur widely in Myrtaceae family and possess variety of biological activities viz. antimicrobial, antimalarial, cancer chemopreventive, anti-HIV and anti-leishmanial. In the present article, quantitative structure-activity relationship (QSAR) study was carried out for a series of phloroglucinol-terpene adducts exhibiting anti-leishmanial activity to find out the structural features which are crucial for the biological activity. The QSAR study was carried out using JChem for Excel and the best QSAR model was derived by multiple regression analysis. The best model of four descriptors yields squared correlation coefficient of 0.930 (s=0.096, F=65.93, Pstudy indicated that the lipophilic character (CLogP), isoelectric point, Haray index and Platt index play important role in anti-leishmanial activity of compounds. Anti-leishmanial activity of several structurally similar naturally occurring euglobals has also been predicted using developed QSAR model. Copyright © 2011 Elsevier Ltd. All rights reserved.

  19. SAR and QSAR studies on the N-terminally acylated pentapeptide agonists for GPR54.

    Science.gov (United States)

    Tomita, Kenji; Oishi, Shinya; Cluzeau, Jérôme; Ohno, Hiroaki; Navenot, Jean-Marc; Wang, Zi-xuan; Peiper, Stephen C; Akamatsu, Miki; Fujii, Nobutaka

    2007-07-12

    Kisspeptins (KPs) play important roles in the regulation of physiological and pathological states through activation of the cognate receptor GPR54. Our previous studies to downsize KP agonists to the essential GPR54 pharmacophore identified peptides 1-3 as low molecular weight GPR54 agonists. In this study, the effect of N-terminal acyl groups on the activity of a series of analogues (R-Phe-Gly-Leu-Arg-Trp-NH2) was investigated in order to develop novel potent GPR54 agonists. Among the compounds developed, the most potent agonistic activity for GPR54 was observed for N-terminal 4-fluorobenzoyl analogue 29. Using quantitative structure-activity relationship studies, it was demonstrated that the inductively negative and small substituents were preferred at the 4-position of N-terminal benzoyl groups.

  20. Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do.

    Science.gov (United States)

    Zhao, Linlin; Wang, Wenyi; Sedykh, Alexander; Zhu, Hao

    2017-06-30

    Numerous chemical data sets have become available for quantitative structure-activity relationship (QSAR) modeling studies. However, the quality of different data sources may be different based on the nature of experimental protocols. Therefore, potential experimental errors in the modeling sets may lead to the development of poor QSAR models and further affect the predictions of new compounds. In this study, we explored the relationship between the ratio of questionable data in the modeling sets, which was obtained by simulating experimental errors, and the QSAR modeling performance. To this end, we used eight data sets (four continuous endpoints and four categorical endpoints) that have been extensively curated both in-house and by our collaborators to create over 1800 various QSAR models. Each data set was duplicated to create several new modeling sets with different ratios of simulated experimental errors (i.e., randomizing the activities of part of the compounds) in the modeling process. A fivefold cross-validation process was used to evaluate the modeling performance, which deteriorates when the ratio of experimental errors increases. All of the resulting models were also used to predict external sets of new compounds, which were excluded at the beginning of the modeling process. The modeling results showed that the compounds with relatively large prediction errors in cross-validation processes are likely to be those with simulated experimental errors. However, after removing a certain number of compounds with large prediction errors in the cross-validation process, the external predictions of new compounds did not show improvement. Our conclusion is that the QSAR predictions, especially consensus predictions, can identify compounds with potential experimental errors. But removing those compounds by the cross-validation procedure is not a reasonable means to improve model predictivity due to overfitting.

  1. Predicting the biological activities through QSAR analysis and docking-based scoring.

    Science.gov (United States)

    Vilar, Santiago; Costanzi, Stefano

    2012-01-01

    Numerous computational methodologies have been developed to facilitate the process of drug discovery. Broadly, they can be classified into ligand-based approaches, which are solely based on the calculation of the molecular properties of compounds, and structure-based approaches, which are based on the study of the interactions between compounds and their target proteins. This chapter deals with two major categories of ligand-based and structure-based methods for the prediction of biological activities of chemical compounds, namely quantitative structure-activity relationship (QSAR) analysis and docking-based scoring. QSAR methods are endowed with robustness and good ranking ability when applied to the prediction of the activity of closely related analogs; however, their great dependence on training sets significantly limits their applicability to the evaluation of diverse compounds. Instead, docking-based scoring, although not very effective in ranking active compounds on the basis of their affinities or potencies, offer the great advantage of not depending on training sets and have proven to be suitable tools for the distinction of active from inactive compounds, thus providing feasible platforms for virtual screening campaigns. Here, we describe the basic principles underlying the prediction of biological activities on the basis of QSAR and docking-based scoring, as well as a method to combine two or more individual predictions into a consensus model. Finally, we describe an example that illustrates the applicability of QSAR and molecular docking to G protein-coupled receptor (GPCR) projects.

  2. Quantitative structure-activity relationship (QSAR) analysis of tumor-specificity of 1,2,3,4-tetrahydroisoquinoline derivatives.

    Science.gov (United States)

    Uesawa, Yoshihiro; Mohri, Kiminori; Kawase, Masami; Ishihara, Mariko; Sakagami, Hiroshi

    2011-12-01

    We have previously reported on the relative cytotoxicity of a total of 38 1,2,3,4-tetrahydroisoquinoline derivatives against human oral squamous cell carcinoma cell lines and human normal oral cells, and the correlation between the cytotoxicity and 17 chemical descriptors. However, the correlation between the tumor-specificity of these compounds and the chemical descriptors has never been investigated so far. Using these previous data, we investigated various parameters for their applicability in predicting tumor specificity. Original data of 50% cytotoxic concentration (CC(50)) values exceeding the maximum concentration in experimental conditions were corrected by the introduction of a harmonic mean, reducing the number of compounds analyzed to 30. The mean pCC(50) (=-log CC(50)) values for normal and tumor cells were defined as N and T, respectively. Tumor specificity was defined as the ratio of the difference of these values to their sum: (T-N)/(T+N). The chemical descriptors were obtained by quantum chemical calculations using semi-empirical (AM1, PM3, and PM6) and density functional theory methods. The relationship between the chemical descriptors and tumor specificity was analyzed by linear regression and artificial neural networks. Out of 17 chemical descriptors, water-accessible surface area showed the highest correlation coefficient with tumor specificity, regardless of the method of calculation. Furthermore, neural network analysis demonstrated the importance of quantum chemical calculations in predicting the specificity of tetrahydroisoquinoline derivatives. The present study suggests the applicability of quantum chemical descriptor in the estimation of tumor specificity of related compounds.

  3. Quantitative structure-activity relationship modeling of the toxicity of organothiophosphate pesticides to Daphnia magna and Cyprinus carpio

    NARCIS (Netherlands)

    Zvinavashe, E.; Du, T.; Griff, T.; Berg, van den J.H.J.; Soffers, A.E.M.F.; Vervoort, J.J.M.; Murk, A.J.; Rietjens, I.

    2009-01-01

    Within the REACH regulatory framework in the EU, quantitative structure-activity relationships (QSAR) models are expected to help reduce the number of animals used for experimental testing. The objective of this study was to develop QSAR models to describe the acute toxicity of organothiophosphate

  4. Receptor-guided 3D-QSAR studies, molecular dynamics simulation and free energy calculations of Btk kinase inhibitors.

    Science.gov (United States)

    Balasubramanian, Pavithra K; Balupuri, Anand; Kang, Hee-Young; Cho, Seung Joo

    2017-03-14

    Bruton tyrosine kinase (Btk) plays an important role in B-cell development, differentiation, and signaling. It is also found be in involved in male immunodeficiency disease such as X-linked agammaglobulinemia (XLA). Btk is considered as a potential therapeutic target for treating autoimmune diseases and hematological malignancies. In this work, a combined molecular modeling study was performed on a series of thieno [3,2-c] pyridine-4-amine derivatives as Btk inhibitors. Receptor-guided COMFA (q (2) = 0.574, NOC = 3, r (2) = 0.924) and COMSIA (q (2) = 0.646, NOC = 6, r (2) = 0.971) models were generated based on the docked conformation of the most active compound 26. All the developed models were tested for robustness using various validation techniques. Furthermore, a 5-ns molecular dynamics (MD) simulation and binding free energy calculations were carried out to determine the binding modes of the inhibitors and to identify crucial interacting residues. The rationality and stability of molecular docking and 3D-QSAR results were validated by MD simulation. The binding free energies calculated by the MM/PBSA method showed the importance of the van der Waals interaction. A good correlation between the MD results, docking studies, and the contour map analysis were observed. The study has identified the key amino acid residues in Btk binding pocket. The results from this study can provide some insights into the development of potent, novel Btk inhibitors.

  5. Combined CoMFA and CoMSIA 3D-QSAR study of benzimidazole and benzothiophene derivatives with selective affinity for the CB2 cannabinoid receptor.

    Science.gov (United States)

    Romero-Parra, Javier; Chung, Hery; Tapia, Ricardo A; Faúndez, Mario; Morales-Verdejo, Cesar; Lorca, Marcos; Lagos, Carlos F; Di Marzo, Vincenzo; David Pessoa-Mahana, C; Mella, Jaime

    2017-04-01

    The preceding years have brought an exponential increase in our understanding of the endocannabinoid system (ECS), including the knowledge of CB1 and CB2 cannabinoid receptors, endocannabinoids, and the enzymes that synthesize and degrade endocannabinoids. Among these ECS components CB2 receptors have been the subject of considerable attention, primarily due to their promising therapeutic potential to treat numerous pathologies while avoiding the adverse psychotropic effects that can accompany CB1 receptor-based therapies. Recently, our research group has reported a new series of non-cytotoxic benzo[d]imidazoles and benzo[b]thiophenes displaying high CB2/CB1 selectivity index. In order to investigate the structural requirements for CB2 ligands and to derive a predictive model that can be used for the design of novel selective CB2 ligands, a three-dimensional quantitative structure-activity relationship (3D-QSAR) study was performed on the above mentioned chemical series employing comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) techniques. The CoMFA and CoMSIA models displayed high external predictability (rpred2 0.919 and 0.908) and good statistical robustness. Valuable information regarding the steric, electrostatic and hydrophobic properties of the molecules was obtained, and several modifications around both heterocycles were evaluated with the aim to generate new promising series of benzo[d]imidazoles and benzo[b]thiophenes derivatives displaying high CB2 selectivity and low toxicity. Copyright © 2017 Elsevier B.V. All rights reserved.

  6. QSAR STUDY OF 1,10-PHENANTHROLINE DERIVATIVES AS THE ANTIMALARIAL COMPOUNDS USING ELECTRONIC DESCRIPTORS BASED ON SEMIEMPIRICAL AM1 CALCULATION

    Directory of Open Access Journals (Sweden)

    Mustofa Mustofa

    2010-06-01

    Full Text Available Quantitative Structure-Activity Relationship (QSAR analysis of 1,10-phenantroline analogs as antimalarial drug has been conducted using atomic net charges (q as predictors of their activity. Data of predictors are obtained from computational chemistry method using semi-empirical molecular orbital AM1 calculation. Antimalarial activities are taken as the activity of the drugs against plasmodium falciparum (FcM29-Cameroun strain and are presented as the value of ln(1/IC50 where IC50 is an effective concentration inhibiting 50 % of the parasite growth.  The results show that there is correlation between antiplasmodial activity and electronic structure as represented by a linear function of activity versus atomic net charges of N1, C7, C10, C14 atoms on the 1,10-phenanthroline skeleton and is expressed by : log IC50 = -3,4398 - 14,9050 qN1 - 8,5589 qC10 - 14,7565 qC7 + 5,0457 qC11 The equation is significant at 95% level with statistical parameters : n = 13; r = 0,96275; r2 = 0,92689; SE = 0,61578 and F (4,8 = 25,3556.   Keywords: antimalarial drug; 1,10-phenanthroline; QSAR; antiplasmodial activity.

  7. An automated framework for QSAR model building.

    Science.gov (United States)

    Kausar, Samina; Falcao, Andre O

    2018-01-16

    In-silico quantitative structure-activity relationship (QSAR) models based tools are widely used to screen huge databases of compounds in order to determine the biological properties of chemical molecules based on their chemical structure. With the passage of time, the exponentially growing amount of synthesized and known chemicals data demands computationally efficient automated QSAR modeling tools, available to researchers that may lack extensive knowledge of machine learning modeling. Thus, a fully automated and advanced modeling platform can be an important addition to the QSAR community. In the presented workflow the process from data preparation to model building and validation has been completely automated. The most critical modeling tasks (data curation, data set characteristics evaluation, variable selection and validation) that largely influence the performance of QSAR models were focused. It is also included the ability to quickly evaluate the feasibility of a given data set to be modeled. The developed framework is tested on data sets of thirty different problems. The best-optimized feature selection methodology in the developed workflow is able to remove 62-99% of all redundant data. On average, about 19% of the prediction error was reduced by using feature selection producing an increase of 49% in the percentage of variance explained (PVE) compared to models without feature selection. Selecting only the models with a modelability score above 0.6, average PVE scores were 0.71. A strong correlation was verified between the modelability scores and the PVE of the models produced with variable selection. We developed an extendable and highly customizable fully automated QSAR modeling framework. This designed workflow does not require any advanced parameterization nor depends on users decisions or expertise in machine learning/programming. With just a given target or problem, the workflow follows an unbiased standard protocol to develop reliable QSAR models

  8. QSAR analysis on Spodoptera litura antifeedant activities for flavone derivatives

    Energy Technology Data Exchange (ETDEWEB)

    Duchowicz, Pablo R., E-mail: pabloducho@gmail.com [Instituto de Investigaciones Fisicoquimicas Teoricas y Aplicadas INIFTA (UNLP, CCT La Plata-CONICET), Diag. 113 y 64, Sucursal 4, C.C. 16, 1900 La Plata (Argentina); Goodarzi, Mohammad [Instituto de Investigaciones Fisicoquimicas Teoricas y Aplicadas INIFTA (UNLP, CCT La Plata-CONICET), Diag. 113 y 64, Sucursal 4, C.C. 16, 1900 La Plata (Argentina); Ocsachoque, Marco A. [Centro de Investigacion y Desarrollo en Ciencias Aplicadas ' Dr. J. J. Ronco' (CINDECA), Departamento de Quimica, Facultad de Ciencias Exactas, UNLP-CONICET. Calle 47 No 257, B1900AJK La Plata (Argentina); Romanelli, Gustavo P. [Centro de Investigacion y Desarrollo en Ciencias Aplicadas ' Dr. J. J. Ronco' (CINDECA), Departamento de Quimica, Facultad de Ciencias Exactas, UNLP-CONICET. Calle 47 No 257, B1900AJK La Plata (Argentina); Catedra de Quimica Organica, Facultad de Ciencias Agrarias y Forestales, UNLP. Calles 60 y 119, B1904AAN La Plata (Argentina); Ortiz, Erlinda del V. [Facultad de Tecnologia y Ciencias Aplicadas, Universidad Nacional de Catamarca, Av. Maximio Victoria 55, (4700), Catamarca (Argentina); Autino, Juan C.; Bennardi, Daniel O.; Ruiz, Diego M. [Catedra de Quimica Organica, Facultad de Ciencias Agrarias y Forestales, UNLP. Calles 60 y 119, B1904AAN La Plata (Argentina); Castro, Eduardo A. [Instituto de Investigaciones Fisicoquimicas Teoricas y Aplicadas INIFTA (UNLP, CCT La Plata-CONICET), Diag. 113 y 64, Sucursal 4, C.C. 16, 1900 La Plata (Argentina)

    2009-12-20

    We establish useful models that relate experimentally measured biological activities of compounds to their molecular structure. The pED{sub 50} feeding inhibition on Spodoptera litura species exhibited by aurones, chromones, 3-coumarones and flavones is analyzed in this work through the hypothesis encompassed in the Quantitative Structure-Activity Relationships (QSAR) Theory. This constitutes a first necessary computationally based step during the design of more bio-friendly repellents that could lead to insights for improving the insecticidal activities of the investigated compounds. After optimizing the molecular structure of each furane and pyrane benzoderivative with the semiempirical molecular orbitals method PM3, more than a thousand of constitutional, topological, geometrical and electronic descriptors are calculated and multiparametric linear regression models are established on the antifeedant potencies. The feature selection method employed in this study is the Replacement Method, which has proven to be successful in previous analyzes. We establish the QSAR both for the complete molecular set of compounds and also for each chemical class, so that acceptably describing the variation of the inhibitory activities from the knowledge of their structure and thus achieving useful predictive results. The main interest of developing trustful QSAR models is that these enable the prediction of compounds having no experimentally measured activities for any reason. Therefore, the structure-activity relationships are further employed for investigating the antifeedant activity on previously synthesized 2-,7-substituted benzopyranes, which do not pose any measured values on the biological expression. One of them, 2-({alpha}-naphtyl)-4H-1-benzopyran-4-one, results in a promising structure to be experimentally analyzed as it has predicted pED{sub 50} = 1.162.

  9. Predictive QSAR modeling of CCR5 antagonist piperidine derivatives using chemometric tools.

    Science.gov (United States)

    Roy, Kunal; Mandal, Asim Sattwa

    2009-02-01

    Quantitative structure-activity relationship (QSAR) studies have been performed on piperidine derivatives (n = 119) as CCR5 antagonists. The whole data set was divided into a training set (75% of the dataset) and a test set (remaining 25%) on the basis of K-means clustering technique. Models developed from the training set were used to assess the predictive potential of the models using test set compounds. Initially classical type QSAR models were developed using structural, spatial, electronic, physicochemical and/or topological parameters using statistical methods like stepwise regression, partial least squares (PLS) and factor analysis followed by multiple linear regression (FA-MLR). Using topological and structural parameters, FA-MLR provided the best equation based on internal validation (Q(2) = 0.514) but the best externally validated model was obtained with PLS ([image omitted] = 0.565). When structural, physicochemical, spatial and electronic descriptors were used, the best Q(2) value (0.562) was obtained from the stepwise regression derived model whereas the best [image omitted] value (0.571) came from the PLS model. When topological descriptors were used in combination with the structural, physicochemical, spatial and electronic descriptors, the best Q(2) and [image omitted] values obtained were 0.530 (stepwise regression) and 0.580 (PLS) respectively. Attempt was made to develop 3D-QSAR models using molecular shape analysis descriptors in combination with structural, physicochemical, spatial and electronic parameters. Linear models were developed using genetic function algorithm coupled with multiple linear regression. However, the results from the 3D-QSAR study were not superior to those of the classical QSAR models. Finally, artificial neural network was employed for development of nonlinear models. The ANN models showed acceptable values of squared correlation coefficient for the observed and predicted values of the test set compounds. From the view

  10. ANN-QSAR model for virtual screening of androstenedione C-skeleton containing phytomolecules and analogues for cytotoxic activity against human breast cancer cell line MCF-7.

    Science.gov (United States)

    Prakash, Om; Khan, Feroz; Sangwan, Rajender Singh; Misra, Laxminarain

    2013-01-01

    The present study deals with the development of an artificial neural network based quantitative structure activity relationship (QSAR) model for virtual screening of active compounds which contain androstenedione carbonskeleton or their similar skeleton at the core. An empirical data modeling (with fitted data mapping) has been performed on the basis of bioassay record for human breast cancer cell line MCF7. The whole experimental data set was considered as test set. Standard feed-forward back-propagation neural network technique was applied to build the model. Leave-One- Out (LOO) cross-validation was performed to evaluate the performance of the model. The mapped model became the basis for selection best mapped compounds followed by development of Pharmacophore specific secondary QSAR model. In the present study, two best mapped molecules '4beta-hydroxy Withanolide-E' and '7, 8-Dehydrocalotropin' were used for development of the secondary QSAR model. These secondary-QSAR models were resulted with R2 LOOCV value 0.9845 and 0.9666 respectively. Docking studies, in silico phamacokinetic and toxicity analysis was also done for selected compounds. The screened compounds CID_73621, CID_16757497, CID_301751, CID_390666 and CID_46830222 were found with promising binding affinity value with aromatase with reference to the co-crystallized control compound androstenedione. Due to excellent extent of variance coverage in ANN based QSAR map model, it can be used as a robust non-linear QSAR model for androstenedione carbon-skeleton containing molecules and the protocol can be used to derive secondary QSAR models for other compounds set.

  11. Modeling Liver-Related Adverse Effects of Drugs Using kNN QSAR Method

    Science.gov (United States)

    Rodgers, Amie D.; Zhu, Hao; Fourches, Dennis; Rusyn, Ivan; Tropsha, Alexander

    2010-01-01

    Adverse effects of drugs (AEDs) continue to be a major cause of drug withdrawals both in development and post-marketing. While liver-related AEDs are a major concern for drug safety, there are few in silico models for predicting human liver toxicity for drug candidates. We have applied the Quantitative Structure Activity Relationship (QSAR) approach to model liver AEDs. In this study, we aimed to construct a QSAR model capable of binary classification (active vs. inactive) of drugs for liver AEDs based on chemical structure. To build QSAR models, we have employed an FDA spontaneous reporting database of human liver AEDs (elevations in activity of serum liver enzymes), which contains data on approximately 500 approved drugs. Approximately 200 compounds with wide clinical data coverage, structural similarity and balanced (40/60) active/inactive ratio were selected for modeling and divided into multiple training/test and external validation sets. QSAR models were developed using the k nearest neighbor method and validated using external datasets. Models with high sensitivity (>73%) and specificity (>94%) for prediction of liver AEDs in external validation sets were developed. To test applicability of the models, three chemical databases (World Drug Index, Prestwick Chemical Library, and Biowisdom Liver Intelligence Module) were screened in silico and the validity of predictions was determined, where possible, by comparing model-based classification with assertions in publicly available literature. Validated QSAR models of liver AEDs based on the data from the FDA spontaneous reporting system can be employed as sensitive and specific predictors of AEDs in pre-clinical screening of drug candidates for potential hepatotoxicity in humans. PMID:20192250

  12. QSAR Investigation on Quinolizidinyl Derivatives in Alzheimer’s Disease

    Directory of Open Access Journals (Sweden)

    Ghasem Ghasemi

    2013-01-01

    Full Text Available Sets of quinolizidinyl derivatives of bi- and tri-cyclic (hetero aromatic systems were studied as selective inhibitors. On the pattern, quantitative structure-activity relationship (QSAR study has been done on quinolizidinyl derivatives as potent inhibitors of acetylcholinesterase in alzheimer’s disease (AD. Multiple linear regression (MLR, partial least squares (PLSs, principal component regression (PCR, and least absolute shrinkage and selection operator (LASSO were used to create QSAR models. Geometry optimization of compounds was carried out by B3LYP method employing 6–31 G basis set. HyperChem, Gaussian 98 W, and Dragon software programs were used for geometry optimization of the molecules and calculation of the quantum chemical descriptors. Finally, Unscrambler program was used for the analysis of data. In the present study, the root mean square error of the calibration and R2 using MLR method were obtained as 0.1434 and 0.95, respectively. Also, the R and R2 values were obtained as 0.79, 0.62 from stepwise MLR model. The R2 and mean square values using LASSO method were obtained as 0.766 and 3.226, respectively. The root mean square error of the calibration and R2 using PLS method were obtained as 0.3726 and 0.62, respectively. According to the obtained results, it was found that MLR model is the most favorable method in comparison with other statistical methods and is suitable for use in QSAR models.

  13. Three dimensional QSAR: applications in pharmacology and toxicology

    National Research Council Canada - National Science Library

    Doucet, Jean-Pierre; Panaye, Annick

    2010-01-01

    ... networks and support vector machines. Three-Dimensional QSAR addresses the scope and limitations of different modeling techniques using case studies from pharmacology, toxicology, and ecotoxicology to demonstrate the utility of each...

  14. Exploring the QSAR's predictive truthfulness of the novel N-tuple discrete derivative indices on benchmark datasets.

    Science.gov (United States)

    Martínez-Santiago, O; Marrero-Ponce, Y; Vivas-Reyes, R; Rivera-Borroto, O M; Hurtado, E; Treto-Suarez, M A; Ramos, Y; Vergara-Murillo, F; Orozco-Ugarriza, M E; Martínez-López, Y

    2017-05-01

    Graph derivative indices (GDIs) have recently been defined over N-atoms (N = 2, 3 and 4) simultaneously, which are based on the concept of derivatives in discrete mathematics (finite difference), metaphorical to the derivative concept in classical mathematical analysis. These molecular descriptors (MDs) codify topo-chemical and topo-structural information based on the concept of the derivative of a molecular graph with respect to a given event (S) over duplex, triplex and quadruplex relations of atoms (vertices). These GDIs have been successfully applied in the description of physicochemical properties like reactivity, solubility and chemical shift, among others, and in several comparative quantitative structure activity/property relationship (QSAR/QSPR) studies. Although satisfactory results have been obtained in previous modelling studies with the aforementioned indices, it is necessary to develop new, more rigorous analysis to assess the true predictive performance of the novel structure codification. So, in the present paper, an assessment and statistical validation of the performance of these novel approaches in QSAR studies are executed, as well as a comparison with those of other QSAR procedures reported in the literature. To achieve the main aim of this research, QSARs were developed on eight chemical datasets widely used as benchmarks in the evaluation/validation of several QSAR methods and/or many different MDs (fundamentally 3D MDs). Three to seven variable QSAR models were built for each chemical dataset, according to the original dissection into training/test sets. The models were developed by using multiple linear regression (MLR) coupled with a genetic algorithm as the feature wrapper selection technique in the MobyDigs software. Each family of GDIs (for duplex, triplex and quadruplex) behaves similarly in all modelling, although there were some exceptions. However, when all families were used in combination, the results achieved were quantitatively

  15. Development and validation of a quantitative structure-activity relationship for chronic narcosis to fish.

    Science.gov (United States)

    Claeys, Lieve; Iaccino, Federica; Janssen, Colin R; Van Sprang, Patrick; Verdonck, Frederik

    2013-10-01

    Vertebrate testing under the European Union's regulation on Registration, Evaluation, Authorisation and Restriction of Chemical substances (REACH) is discouraged, and the use of alternative nontesting approaches such as quantitative structure-activity relationships (QSARs) is encouraged. However, robust QSARs predicting chronic ecotoxicity of organic compounds to fish are not available. The Ecological Structure Activity Relationships (ECOSAR) Class Program is a computerized predictive system that estimates the acute and chronic toxicity of organic compounds for several chemical classes based on their log octanol-water partition coefficient (K(OW)). For those chemical classes for which chronic training data sets are lacking, acute to chronic ratios are used to predict chronic toxicity to aquatic organisms. Although ECOSAR reaches a high score against the Organisation for Economic Co-operation and Development (OECD) principles for QSAR validation, the chronic QSARs in ECOSAR are not fully compliant with OECD criteria in the framework of REACH or CLP (classification, labeling, and packaging) regulation. The objective of the present study was to develop a chronic ecotoxicity QSAR for fish for compounds acting via nonpolar and polar narcosis. These QSARs were built using a database of quality screened toxicity values, considering only chronic exposure durations and relevant end points. After statistical multivariate diagnostic analysis, literature-based, mechanistically relevant descriptors were selected to develop a multivariate regression model. Finally, these QSARs were tested for their acceptance for regulatory purposes and were found to be compliant with the OECD principles for the validation of a QSAR. © 2013 SETAC.

  16. Daphnia and fish toxicity of (benzo)triazoles: validated QSAR models, and interspecies quantitative activity-activity modelling.

    Science.gov (United States)

    Cassani, Stefano; Kovarich, Simona; Papa, Ester; Roy, Partha Pratim; van der Wal, Leon; Gramatica, Paola

    2013-08-15

    Due to their chemical properties synthetic triazoles and benzo-triazoles ((B)TAZs) are mainly distributed to the water compartments in the environment, and because of their wide use the potential effects on aquatic organisms are cause of concern. Non testing approaches like those based on quantitative structure-activity relationships (QSARs) are valuable tools to maximize the information contained in existing experimental data and predict missing information while minimizing animal testing. In the present study, externally validated QSAR models for the prediction of acute (B)TAZs toxicity in Daphnia magna and Oncorhynchus mykiss have been developed according to the principles for the validation of QSARs and their acceptability for regulatory purposes, proposed by the Organization for Economic Co-operation and Development (OECD). These models are based on theoretical molecular descriptors, and are statistically robust, externally predictive and characterized by a verifiable structural applicability domain. They have been applied to predict acute toxicity for over 300 (B)TAZs without experimental data, many of which are in the pre-registration list of the REACH regulation. Additionally, a model based on quantitative activity-activity relationships (QAAR) has been developed, which allows for interspecies extrapolation from daphnids to fish. The importance of QSAR/QAAR, especially when dealing with specific chemical classes like (B)TAZs, for screening and prioritization of pollutants under REACH, has been highlighted. Copyright © 2013 Elsevier B.V. All rights reserved.

  17. Radial basis function network-based transform for a nonlinear support vector machine as optimized by a particle swarm optimization algorithm with application to QSAR studies.

    Science.gov (United States)

    Tang, Li-Juan; Zhou, Yan-Ping; Jiang, Jian-Hui; Zou, Hong-Yan; Wu, Hai-Long; Shen, Guo-Li; Yu, Ru-Qin

    2007-01-01

    The support vector machine (SVM) has been receiving increasing interest in an area of QSAR study for its ability in function approximation and remarkable generalization performance. However, selection of support vectors and intensive optimization of kernel width of a nonlinear SVM are inclined to get trapped into local optima, leading to an increased risk of underfitting or overfitting. To overcome these problems, a new nonlinear SVM algorithm is proposed using adaptive kernel transform based on a radial basis function network (RBFN) as optimized by particle swarm optimization (PSO). The new algorithm incorporates a nonlinear transform of the original variables to feature space via a RBFN with one input and one hidden layer. Such a transform intrinsically yields a kernel transform of the original variables. A synergetic optimization of all parameters including kernel centers and kernel widths as well as SVM model coefficients using PSO enables the determination of a flexible kernel transform according to the performance of the total model. The implementation of PSO demonstrates a relatively high efficiency in convergence to a desired optimum. Applications of the proposed algorithm to QSAR studies of binding affinity of HIV-1 reverse transcriptase inhibitors and activity of 1-phenylbenzimidazoles reveal that the new algorithm provides superior performance to the backpropagation neural network and a conventional nonlinear SVM, indicating that this algorithm holds great promise in nonlinear SVM learning.

  18. Organic Micropollutants Removal from Water by Oxidation and Other Processes:QSAR Models, Decision Support System and Hybrids of Processes

    KAUST Repository

    Sudhakaran, Sairam

    2013-08-01

    The presence of organic micropollutants (OMPs) in water is of great environmental concern. OMPs such as endocrine disruptors and certain pharmaceuticals have shown alarming effects on aquatic life. OMPs are included in the priority list of contaminants in several government directorate frameworks. The low levels of OMPs concentration (ng/L to μg/L) force the use of sophisticated analytical instruments. Although, the techniques to detect OMPs are progressing, the focus of current research is only on limited, important OMPs due to the high amount of time, cost and effort involved in analyzing them. Alternatively, quantitative structure activity relationship (QSAR) models help to screen processes and propose appropriate options without considerable experimental effort. QSAR models are well-established in regulatory bodies as a method to screen toxic chemicals. The goal of the present thesis was to develop QSAR models for OMPs removal by oxidation. Apart from the QSAR models, a decision support system (DSS) based on multi-criteria analysis (MCA) involving socio-economic-technical and sustainability aspects was developed. Also, hybrids of different water treatment processes were studied to propose a sustainable water treatment train for OMPs removal. In order to build the QSAR models, the ozone/hydroxyl radical rate constants or percent removals of the OMPs were compiled. Several software packages were used to 5 compute the chemical properties of OMPs and perform statistical analyses. For DSS, MCA was used since it allows the comparison of qualitative (non-monetary, non-metric) and quantitative criteria (e.g., costs). Quadrant plots were developed to study the hybrid of natural and advanced water treatment processes. The QSAR models satisfied both chemical and statistical criteria. The DSS resulted in natural treatment and ozonation as the preferred processes for OMPs removal. The QSAR models can be used as a screening tool for OMPs removal by oxidation. Moreover, the

  19. Improving the applicability of (Q)SARs for percutaneous penetration in regulatory risk assessment.

    NARCIS (Netherlands)

    Bouwman, T.; Cronin, M.T.; Bessems, J.G.; Sandt, J.J. van de

    2008-01-01

    The new regulatory framework REACH (Registration, Evaluation, and Authorisation of Chemicals) foresees the use of non-testing approaches, such as read-across, chemical categories, structure-activity relationships (SARs) and quantitative structure-activity relationships (QSARs). Although information

  20. Prediction of anticancer property of bowsellic acid derivatives by quantitative structure activity relationship analysis and molecular docking study.

    Science.gov (United States)

    Satpathy, Raghunath; Guru, R K; Behera, R; Nayak, B

    2015-01-01

    Boswellic acid consists of a series of pentacyclic triterpene molecules that are produced by the plant Boswellia serrata. The potential applications of Bowsellic acid for treatment of cancer have been focused here. To predict the property of the bowsellic acid derivatives as anticancer compounds by various computational approaches. In this work, all total 65 derivatives of bowsellic acids from the PubChem database were considered for the study. After energy minimization of the ligands various types of molecular descriptors were computed and corresponding two-dimensional quantitative structure activity relationship (QSAR) models were obtained by taking Andrews coefficient as the dependent variable. Different types of comparative analysis were used for QSAR study are multiple linear regression, partial least squares, support vector machines and artificial neural network. From the study geometrical descriptors shows the highest correlation coefficient, which indicates the binding factor of the compound. To evaluate the anticancer property molecular docking study of six selected ligands based on Andrews affinity were performed with nuclear factor-kappa protein kinase (Protein Data Bank ID 4G3D), which is an established therapeutic target for cancers. Along with QSAR study and docking result, it was predicted that bowsellic acid can also be treated as a potential anticancer compound. Along with QSAR study and docking result, it was predicted that bowsellic acid can also be treated as a potential anticancer compound.

  1. Mining Discriminative Patterns from Graph Data with Multiple Labels and Its Application to Quantitative Structure-Activity Relationship (QSAR) Models.

    Science.gov (United States)

    Shao, Zheng; Hirayama, Yuya; Yamanishi, Yoshihiro; Saigo, Hiroto

    2015-12-28

    Graph data are becoming increasingly common in machine learning and data mining, and its application field pervades to bioinformatics and cheminformatics. Accordingly, as a method to extract patterns from graph data, graph mining recently has been studied and developed rapidly. Since the number of patterns in graph data is huge, a central issue is how to efficiently collect informative patterns suitable for subsequent tasks such as classification or regression. In this paper, we consider mining discriminative subgraphs from graph data with multiple labels. The resulting task has important applications in cheminformatics, such as finding common functional groups that trigger multiple drug side effects, or identifying ligand functional groups that hit multiple targets. In computational experiments, we first verify the effectiveness of the proposed approach in synthetic data, then we apply it to drug adverse effect prediction problem. In the latter dataset, we compared the proposed method with L1-norm logistic regression in combination with the PubChem/Open Babel fingerprint, in that the proposed method showed superior performance with a much smaller number of subgraph patterns. Software is available from https://github.com/axot/GLP.

  2. Development of a QSAR Model for Thyroperoxidase Inhbition ...

    Science.gov (United States)

    hyroid hormones (THs) are involved in multiple biological processes and are critical modulators of fetal development. Even moderate changes in maternal or fetal TH levels can produce irreversible neurological deficits in children, such as lower IQ. The enzyme thyroperoxidase (TPO) plays a key role in the synthesis of THs, and inhibition of TPO by xenobiotics results in decreased TH synthesis. Recently, a high-throughput screening assay for TPO inhibition (AUR-TPO) was developed and used to test the ToxCast Phase I and II chemicals. In the present study, we used the results from AUR-TPO to develop a Quantitative Structure-Activity Relationship (QSAR) model for TPO inhibition. The training set consisted of 898 discrete organic chemicals: 134 inhibitors and 764 non-inhibitors. A five times two-fold cross-validation of the model was performed, yielding a balanced accuracy of 78.7%. More recently, an additional ~800 chemicals were tested in the AUR-TPO assay. These data were used for a blinded external validation of the QSAR model, demonstrating a balanced accuracy of 85.7%. Overall, the cross- and external validation indicate a robust model with high predictive performance. Next, we used the QSAR model to predict 72,526 REACH pre-registered substances. The model could predict 49.5% (35,925) of the substances in its applicability domain and of these, 8,863 (24.7%) were predicted to be TPO inhibitors. Predictions from this screening can be used in a tiered approach to

  3. The Role of Feature Selection and Statistical Weighting in Predicting In Vivo Toxicity Using In Vitro Assay and QSAR Data (SOT)

    Science.gov (United States)

    Our study assesses the value of both in vitro assay and quantitative structure activity relationship (QSAR) data in predicting in vivo toxicity using numerous statistical models and approaches to process the data. Our models are built on datasets of (i) 586 chemicals for which bo...

  4. Quantitative structure-activity relationship and molecular docking studies on designing inhibitors of the perforin.

    Science.gov (United States)

    Song, Fucheng; Cui, Lianhua; Piao, Jinmei; Liang, Hui; Si, Hongzong; Duan, Yunbo; Zhai, Honglin

    2017-10-01

    Quantitative structure-activity relationship (QSAR) studies were performed on a series of 5-arylidene-2thioxoimidazolidin-4-ones derivatives as the inhibitors of perforin and to gain insights about the structural determinants for designing new drug molecules. The heuristic method could explore the descriptors responsible for bioactivity and gain a best linear model with R2 .82. Gene expression programming method generated a novel nonlinear function model with R2 .92 for training set and R2 .85 for test set. The predicted IC50 by QSAR, molecular docking analysis, and property explorer applet show that 42a acts as a well-pleasing potent inhibitor for perforin. This study may lay a reliable theoretical foundation for the development of designing perforin inhibitor structures. © 2017 John Wiley & Sons A/S.

  5. Modelling the effect of structural QSAR parameters on skin penetration using genetic programming

    Science.gov (United States)

    Chung, K. K.; Do, D. Q.

    2010-09-01

    In order to model relationships between chemical structures and biological effects in quantitative structure-activity relationship (QSAR) data, an alternative technique of artificial intelligence computing—genetic programming (GP)—was investigated and compared to the traditional method—statistical. GP, with the primary advantage of generating mathematical equations, was employed to model QSAR data and to define the most important molecular descriptions in QSAR data. The models predicted by GP agreed with the statistical results, and the most predictive models of GP were significantly improved when compared to the statistical models using ANOVA. Recently, artificial intelligence techniques have been applied widely to analyse QSAR data. With the capability of generating mathematical equations, GP can be considered as an effective and efficient method for modelling QSAR data.

  6. QUANTITATIVE ELECTRONIC STRUCTURE - ACTIVITY RELATIONSHIP OF ANTIMALARIAL COMPOUND OF ARTEMISININ DERIVATIVES USING PRINCIPAL COMPONENT REGRESSION APPROACH

    Directory of Open Access Journals (Sweden)

    Paul Robert Martin Werfette

    2010-06-01

    Full Text Available Analysis of quantitative structure - activity relationship (QSAR for a series of antimalarial compound artemisinin derivatives has been done using principal component regression. The descriptors for QSAR study were representation of electronic structure i.e. atomic net charges of the artemisinin skeleton calculated by AM1 semi-empirical method. The antimalarial activity of the compound was expressed in log 1/IC50 which is an experimental data. The main purpose of the principal component analysis approach is to transform a large data set of atomic net charges to simplify into a data set which known as latent variables. The best QSAR equation to analyze of log 1/IC50 can be obtained from the regression method as a linear function of several latent variables i.e. x1, x2, x3, x4 and x5. The best QSAR model is expressed in the following equation,  (;;   Keywords: QSAR, antimalarial, artemisinin, principal component regression

  7. Quantitative structure-activity relationships for cellular uptake of surface-modified nanoparticles.

    Science.gov (United States)

    Liu, Rong; Rallo, Robert; Bilal, Muhammad; Cohen, Yoram

    2015-01-01

    Quantitative structure-activity relationships (QSARs) were developed, for cellular uptake of nanoparticles (NPs) of the same iron oxide core but with different surface-modifying organic molecules, based on linear and non-linear (epsilon support vector regression (ε-SVR)). A linear QSAR provided high prediction accuracy of R2=0.751 (coefficient of determination) using 11 descriptors selected from an initial pool of 184 descriptors calculated for the NP surfacemodifying molecules, while a ε-SVR based QSAR with only 6 descriptors improved prediction accuracy to R2=0.806. The linear and ε-SVR based QSARs both demonstrated good robustness and well spanned applicability domains. It is suggested that the approach of evaluating pertinent descriptors and their significance, via QSAR analysis, to cellular NP uptake could support planning and interpretation of toxicity studies as well as provide guidance for the tailor-design NPs with respect to targeted cellular uptake for various applications.

  8. Biological activities of triazine derivatives. Combining DFT and QSAR results

    Directory of Open Access Journals (Sweden)

    Majdouline Larif

    2017-02-01

    Full Text Available In order to investigate the relationship between activities and structures, a 3D-QSAR study is applied to a set of 43 molecules based on triazines. This study was conducted using the principal component analysis (PCA method, the multiple linear regression method (MLR and the artificial neural network (ANN. The predicted values of activities are in good agreement with the experimental results. The artificial neural network (ANN techniques, considering the relevant descriptors obtained from the MLR, showed a correlation coefficient of 0.9 with an 8-3-1 ANN model which is a good result. As a result of quantitative structure–activity relationships, we found that the model proposed in this study is constituted of major descriptors used to describe these molecules. The obtained results suggested that the proposed combination of several calculated parameters could be useful to predict the biological activity of triazine derivatives.

  9. Ensemble QSAR: a QSAR method based on conformational ensembles and metric descriptors.

    Science.gov (United States)

    Pissurlenkar, Raghuvir R S; Khedkar, Vijay M; Iyer, Radhakrishnan P; Coutinho, Evans C

    2011-07-30

    Quantitative structure-activity relationship (QSAR) is the most versatile tool in computer-assisted molecular design. One conceptual drawback seen in QSAR approaches is the "one chemical-one structure-one parameter value" dogma where the model development is based on physicochemical description for a single molecular conformation, while ignoring the rest of the conformational space. It is well known that molecules have several low-energy conformations populated at physiological temperature, and each conformer makes a significant impact on associated properties such as biological activity. At the level of molecular interaction, the dynamics around the molecular structure is of prime essence rather than the average structure. As a step toward understanding the role of these discrete microscopic states in biological activity, we have put together a theoretically rigorous and computationally tractable formalism coined as eQSAR. In this approach, the biological activity is modeled as a function of physicochemical description for a selected set of low-energy conformers, rather than that's for a single lowest energy conformation. Eigenvalues derived from the "Physicochemical property integrated distance matrices" (PD-matrices) that encompass both 3D structure and physicochemical properties, have been used as descriptors; is a novel addition. eQSAR is validated on three peptide datasets and explicitly elaborated for bradykinin-potentiating peptides. The conformational ensembles were generated by a simple molecular dynamics and consensus dynamics approaches. The eQSAR models are statistically significant and possess the ability to select the most biologically relevant conformation(s) with the relevant physicochemical attributes that have the greatest meaning for description of the biological activity. Copyright © 2011 Wiley Periodicals, Inc.

  10. A SAR and QSAR Study of New Artemisinin Compounds with Antimalarial Activity

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    Cleydson Breno R. Santos

    2013-12-01

    Full Text Available The Hartree-Fock method and the 6-31G** basis set were employed to calculate the molecular properties of artemisinin and 20 derivatives with antimalarial activity. Maps of molecular electrostatic potential (MEPs and molecular docking were used to investigate the interaction between ligands and the receptor (heme. Principal component analysis and hierarchical cluster analysis were employed to select the most important descriptors related to activity. The correlation between biological activity and molecular properties was obtained using the partial least squares and principal component regression methods. The regression PLS and PCR models built in this study were also used to predict the antimalarial activity of 30 new artemisinin compounds with unknown activity. The models obtained showed not only statistical significance but also predictive ability. The significant molecular descriptors related to the compounds with antimalarial activity were the hydration energy (HE, the charge on the O11 oxygen atom (QO11, the torsion angle O1-O2-Fe-N2 (D2 and the maximum rate of R/Sanderson Electronegativity (RTe+. These variables led to a physical and structural explanation of the molecular properties that should be selected for when designing new ligands to be used as antimalarial agents.

  11. QSAR models for the prediction of plasma protein binding.

    Science.gov (United States)

    Ghafourian, Taravat; Amin, Zeshan

    2013-01-01

    The prediction of plasma protein binding (ppb) is of paramount importance in the pharmacokinetics characterization of drugs, as it causes significant changes in volume of distribution, clearance and drug half life. This study utilized Quantitative Structure - Activity Relationships (QSAR) for the prediction of plasma protein binding. Protein binding values for 794 compounds were collated from literature. The data was partitioned into a training set of 662 compounds and an external validation set of 132 compounds. Physicochemical and molecular descriptors were calculated for each compound using ACD labs/logD, MOE (Chemical Computing Group) and Symyx QSAR software packages. Several data mining tools were employed for the construction of models. These included stepwise regression analysis, Classification and Regression Trees (CART), Boosted trees and Random Forest. Several predictive models were identified; however, one model in particular produced significantly superior prediction accuracy for the external validation set as measured using mean absolute error and correlation coefficient. The selected model was a boosted regression tree model which had the mean absolute error for training set of 13.25 and for validation set of 14.96. Plasma protein binding can be modeled using simple regression trees or multiple linear regressions with reasonable model accuracies. These interpretable models were able to identify the governing molecular factors for a high ppb that included hydrophobicity, van der Waals surface area parameters, and aromaticity. On the other hand, the more complicated ensemble method of boosted regression trees produced the most accurate ppb estimations for the external validation set.

  12. QSAR models for antioxidant activity of new coumarin derivatives.

    Science.gov (United States)

    Erzincan, P; Saçan, M T; Yüce-Dursun, B; Danış, Ö; Demir, S; Erdem, S S; Ogan, A

    2015-01-01

    This study presents 37 new antioxidant coumarin derivatives and strategies for structural modification to improve their antioxidant activities, the main ferric-reducing antioxidant power (FRAP) assay used to evaluate their antioxidant properties and the generation of validated quantitative structure-activity (antioxidant activity) relationship (QSAR) models. In an attempt to generate QSAR models, structures of all coumarin derivatives in the data set were fully optimized by semi-empirical PM6 method using SPARTAN 10 software. Descriptors were calculated by DRAGON 6.0 software. Multiple linear regression (MLR) models were developed with different training/test set combinations using QSARINS 2.2.1 software. Robustness, reliability and predictive power of the models were tested by internal and external validations. Applicability domain of the best two-descriptor model (nTR = 30; r(2) = 0.924; RMSETR = 0.213; nTEST = 7; r(2)ext = 0.887; RMSEext = 0.255; CCCext = 0.939) was determined. Descriptors appeared in the model revealed that complexity, H-bond donor and lipophilic character are important parameters in describing the antioxidant activity. Apart from the compounds in the data set, we also designed 31 new antioxidant coumarin derivatives and predicted their antioxidant activity using the best two-descriptor model. Most of these compounds are promising antioxidants.

  13. Prediction of new Hsp90 inhibitors based on 3,4-isoxazolediamide scaffold using QSAR study, molecular docking and molecular dynamic simulation.

    Science.gov (United States)

    Abbasi, Maryam; Sadeghi-Aliabadi, Hojjat; Amanlou, Massoud

    2017-06-30

    Heat shock protein90 (Hsp90) are overexpressed in tumor cells, so the inhibition of the Hsp90 ATPase activity would be a significantly effective strategy in cancer therapy. In the current study, 3,4-isoxazolediamide derivatives were suggested as an Hsp90 inhibitor for anti-cancer therapy. Multiple linear regression (MLR) and genetic algorithm of partial least square (GA-PLS) methods were performed to build models to predict the inhibitory activity of Hsp90. The leave-one out (LOO) cross-validation and Y-randomization tests were performed to models' validation. The new ligands were monitored by applicability domain. Molecular docking studies were also conducted to evaluate the mode of interaction of these compounds with Hsp90. Identification of the likely pathways into the active site pocket and the involved residues were performed by CAVAER 3.0.1 software. According to QSAR models and docking analysis, three new compounds were predicted. 50 ns molecular dynamic simulation was performed for the strongest synthesized compound and the best predicted compound in terms of binding energy and interactions between ligand and protein. The made models showed the significance of size, shape, symmetry, and branching of molecules in inhibitory activities of Hsp90. Docking studies indicated that two hydroxyl groups in the resorcinol ring were important in interacting with Asp93 and the orientation of these groups was related to substitution of different R1 groups. Comparing of molecular dynamic simulation (MDs) results shows that new compound perched in active site with lower binding energy than the best synthesized compound. The QSAR and docking analyses shown to be beneficial tools in the proposal of anti-cancer activities and a leader to the synthesis of new Hsp90 inhibitors based 3,4-isoxazolediamide. The MDs confirmed that predicted ligand is steady in the Hsp90 active sites.

  14. Virtual screening of B-Raf kinase inhibitors: A combination of pharmacophore modelling, molecular docking, 3D-QSAR model and binding free energy calculation studies.

    Science.gov (United States)

    Zhang, Wen; Qiu, Kai-Xiong; Yu, Fang; Xie, Xiao-Guang; Zhang, Shu-Qun; Chen, Ya-Juan; Xie, Hui-Ding

    2017-10-01

    B-Raf kinase has been identified as an important target in recent cancer treatment. In order to discover structurally diverse and novel B-Raf inhibitors (BRIs), a virtual screening of BRIs against ZINC database was performed by using a combination of pharmacophore modelling, molecular docking, 3D-QSAR model and binding free energy (ΔGbind) calculation studies in this work. After the virtual screening, six promising hit compounds were obtained, which were then tested for inhibitory activities of A375 cell lines. In the result, five hit compounds show good biological activities (IC50<50μM). The present method of virtual screening can be applied to find structurally diverse inhibitors, and the obtained five structurally diverse compounds are expected to develop novel BRIs. Copyright © 2017. Published by Elsevier Ltd.

  15. Synthesis, antimicrobial, anticancer, antiviral evaluation and QSAR studies of 4-(1-aryl-2-oxo-1,2-dihydro-indol-3-ylideneamino-N-substituted benzene sulfonamides

    Directory of Open Access Journals (Sweden)

    Mahesh Kumar

    2014-09-01

    Full Text Available A series of 4-(1-aryl-2-oxo-1,2-dihydro-indol-3-ylideneamino-N-substituted benzenesulfonamide derivatives (1–32 was synthesized and evaluated for its in vitro antimicrobial, antiviral and cytotoxic activities. Antimicrobial results indicated that compounds (11 and (18 were found to be the most effective ones. In general, the synthesized compounds were bacteriostatic and fungistatic in their action. The cytotoxic screening results indicated that the compounds were less active than the standard drug 5-fluorouracil (5-FU. None of the compounds inhibited viral replication at subtoxic concentrations. In general, the presence of a pyrimidine ring with electron releasing groups and an ortho- and para-substituted benzoyl moiety favored antimicrobial activities. The results of QSAR studies demonstrated the importance of topological parameters, valence zero order molecular connectivity index (0χv and valence first order molecular connectivity index (1χv in describing the antimicrobial activity of synthesized compounds.

  16. Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.

    Science.gov (United States)

    Eriksson, Lennart; Jaworska, Joanna; Worth, Andrew P; Cronin, Mark T D; McDowell, Robert M; Gramatica, Paola

    2003-08-01

    This article provides an overview of methods for reliability assessment of quantitative structure-activity relationship (QSAR) models in the context of regulatory acceptance of human health and environmental QSARs. Useful diagnostic tools and data analytical approaches are highlighted and exemplified. Particular emphasis is given to the question of how to define the applicability borders of a QSAR and how to estimate parameter and prediction uncertainty. The article ends with a discussion regarding QSAR acceptability criteria. This discussion contains a list of recommended acceptability criteria, and we give reference values for important QSAR performance statistics. Finally, we emphasize that rigorous and independent validation of QSARs is an essential step toward their regulatory acceptance and implementation.

  17. Pharmacophore Modelling and 4d-Qsar Study Of Ruthenium(Ii) Arene Complexes As Anticancer Agents (Inhibitors) By Electron Conformational-Genetic Algorithm Method.

    Science.gov (United States)

    Yavuz, Sevtap Çağlar; Sabancı, Nazmiye; Sarıpınar, Emin

    2017-05-28

    The EC-GA method was employed in this study as a 4D-QSAR method, for the identification of the pharmacophore (Pha) of ruthenium(II) arene complex derivatives and quantitative prediction of activity. The arrangement of the computed geometric and electronic parameters for atoms and bonds of each compound occurring in a matrix is known as the electron-conformational matrix of congruity (ECMC). It contains the data from HF/3-21G level calculations. Compounds were represented by a group of conformers for each compound rather than a single conformation, known as fourth dimension to generate the model. ECMCs were compared within a certain range of tolerance values by using the EMRE program and the responsible pharmacophore group for ruthenium(II) arene complex derivatives was found. For selecting the sub-parameter which had the most effect on activity in the series and the calculation of theoretical activity values, the non-linear least square method and genetic algorithm which are included in the EMRE program were used. In addition, compounds were classified as the training and test set and the accuracy of the models was tested by cross-validation statistically. The model for training and test sets attained by the optimum 10 parameters gave highly satisfactory results with R2training= 0.817, q 2=0.718 and SEtraining=0.066, q2ext1 = 0.867, q2ext2 = 0.849, q2ext3 =0.895, ccctr = 0.895, ccctest = 0.930 and cccall = 0.905. Since there is no 4D-QSAR research on metal based organic complexes in the literature, this study is original and supply a powerful tool to the design of novel and selective ruthenium(II) arene complexes. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  18. QSAR prediction of the competitive interaction of emerging halogenated pollutants with human transthyretin.

    Science.gov (United States)

    Papa, E; Kovarich, S; Gramatica, P

    2013-01-01

    The determination of the potential endocrine disruption (ED) activity of chemicals such as poly/perfluorinated compounds (PFCs) and brominated flame retardants (BFRs) is still hindered by a limited availability of experimental data. Quantitative structure-activity relationship (QSAR) strategies can be applied to fill this data gap, help in the characterization of the ED potential, and screen PFCs and BFRs with a hazardous toxicological profile. This paper proposes the modelling of T4-TTR (thyroxin-transthyretin) competing potency and relative binding potency toward T4 (logT4-REP) of PFCs and BFRs by regression and classification QSAR models. This study is a follow up of a former work, which analysed separately the interaction of BFRs and PFCs with the carrier TTR. The new results demonstrate the possibility of developing robust and predictive QSARs, which include both BFRs and PFCs in the training set, obtaining larger applicability domains than the existing models developed separately for BFRs and PFCs. The selection of modelling molecular descriptors confirms the importance of structural features, such as the aromatic OH or the molecular length, to increase the binding of the studied chemicals to TTR. Additionally, the need of experimental tests for some chemicals, and in particular for some of the BFRs, is highlighted.

  19. Investigations on Inhibitors of Hedgehog Signal Pathway: A Quantitative Structure-Activity Relationship Study

    Directory of Open Access Journals (Sweden)

    Zhiwei Cao

    2011-05-01

    Full Text Available The hedgehog signal pathway is an essential agent in developmental patterning, wherein the local concentration of the Hedgehog morphogens directs cellular differentiation and expansion. Furthermore, the Hedgehog pathway has been implicated in tumor/stromal interaction and cancer stem cell. Nowadays searching novel inhibitors for Hedgehog Signal Pathway is drawing much more attention by biological, chemical and pharmological scientists. In our study, a solid computational model is proposed which incorporates various statistical analysis methods to perform a Quantitative Structure-Activity Relationship (QSAR study on the inhibitors of Hedgehog signaling. The whole QSAR data contain 93 cyclopamine derivatives as well as their activities against four different cell lines (NCI-H446, BxPC-3, SW1990 and NCI-H157. Our extensive testing indicated that the binary classification model is a better choice for building the QSAR model of inhibitors of Hedgehog signaling compared with other statistical methods and the corresponding in silico analysis provides three possible ways to improve the activity of inhibitors by demethylation, methylation and hydroxylation at specific positions of the compound scaffold respectively. From these, demethylation is the best choice for inhibitor structure modifications. Our investigation also revealed that NCI-H466 served as the best cell line for testing the activities of inhibitors of Hedgehog signal pathway among others.

  20. Structure-based approach to pharmacophore identification, in silico screening, and three-dimensional quantitative structure-activity relationship studies for inhibitors of Trypanosoma cruzi dihydrofolate reductase function

    Energy Technology Data Exchange (ETDEWEB)

    Schormann, N.; Senkovich, O.; Walker, K.; Wright, D.L.; Anderson, A.C.; Rosowsky, A.; Ananthan, S.; Shinkre, B.; Velu, S.; Chattopadhyay, D. (UAB); (Connecticut); (Southern Research); (DFCI)

    2009-07-10

    We have employed a structure-based three-dimensional quantitative structure-activity relationship (3D-QSAR) approach to predict the biochemical activity for inhibitors of T. cruzi dihydrofolate reductase-thymidylate synthase (DHFR-TS). Crystal structures of complexes of the enzyme with eight different inhibitors of the DHFR activity together with the structure in the substrate-free state (DHFR domain) were used to validate and refine docking poses of ligands that constitute likely active conformations. Structural information from these complexes formed the basis for the structure-based alignment used as input for the QSAR study. Contrary to indirect ligand-based approaches the strategy described here employs a direct receptor-based approach. The goal is to generate a library of selective lead inhibitors for further development as antiparasitic agents. 3D-QSAR models were obtained for T. cruzi DHFR-TS (30 inhibitors in learning set) and human DHFR (36 inhibitors in learning set) that show a very good agreement between experimental and predicted enzyme inhibition data. For crossvalidation of the QSAR model(s), we have used the 10% leave-one-out method. The derived 3D-QSAR models were tested against a few selected compounds (a small test set of six inhibitors for each enzyme) with known activity, which were not part of the learning set, and the quality of prediction of the initial 3D-QSAR models demonstrated that such studies are feasible. Further refinement of the models through integration of additional activity data and optimization of reliable docking poses is expected to lead to an improved predictive ability.

  1. QSAR models for the removal of organic micropollutants in four different river water matrices

    KAUST Repository

    Sudhakaran, Sairam

    2012-04-01

    Ozonation is an advanced water treatment process used to remove organic micropollutants (OMPs) such as pharmaceuticals and personal care products (PPCPs). In this study, Quantitative Structure Activity Relationship (QSAR) models, for ozonation and advanced oxidation process (AOP), were developed with percent-removal of OMPs by ozonation as the criterion variable. The models focused on PPCPs and pesticides elimination in bench-scale studies done within natural water matrices: Colorado River, Passaic River, Ohio River and Suwannee synthetic water. The OMPs removal for the different water matrices varied depending on the water quality conditions such as pH, DOC, alkalinity. The molecular descriptors used to define the OMPs physico-chemical properties range from one-dimensional (atom counts) to three-dimensional (quantum-chemical). Based on a statistical modeling approach using more than 40 molecular descriptors as predictors, descriptors influencing ozonation/AOP were chosen for inclusion in the QSAR models. The modeling approach was based on multiple linear regression (MLR). Also, a global model based on neural networks was created, compiling OMPs from all the four river water matrices. The chemically relevant molecular descriptors involved in the QSAR models were: energy difference between lowest unoccupied and highest occupied molecular orbital (E LUMO-E HOMO), electron-affinity (EA), number of halogen atoms (#X), number of ring atoms (#ring atoms), weakly polar component of the solvent accessible surface area (WPSA) and oxygen to carbon ratio (O/C). All the QSAR models resulted in a goodness-of-fit, R 2, greater than 0.8. Internal and external validations were performed on the models. © 2011 Elsevier Ltd.

  2. QSAR model as a random event: A case of rat toxicity.

    Science.gov (United States)

    Toropova, Alla P; Toropov, Andrey A; Benfenati, Emilio; Leszczynska, Danuta; Leszczynski, Jerzy

    2015-03-15

    Quantitative structure-property/activity relationships (QSPRs/QSARs) can be used to predict physicochemical and/or biochemical behavior of substances which were not studied experimentally. Typically predicted values for chemicals in the training set are accurate since they were used to build the model. QSPR/QSAR models must be validated before they are used in practice. Unfortunately, the majority of the suggested approaches of the validation of QSPR/QSAR models are based on consideration of geometrical features of clusters of data points in the plot of experimental versus calculated values of an endpoint. We believe these geometrical criteria can be more useful if they are analyzed for several splits into the training and test sets. In this way, one can estimate the reproducibility of the model with various splits and better evaluate model reliability. The probability of the correct prediction of an endpoint for external validation set (in the series of the above-mentioned splits) can provide an useful way to evaluate the domain of applicability of the model. Copyright © 2015 Elsevier Ltd. All rights reserved.

  3. Exploring QSAR of Non-Nucleoside Reverse Transcriptase Inhibitors by Neural Networks: TIBO Derivatives

    Directory of Open Access Journals (Sweden)

    Driss Cherqaoui

    2004-01-01

    Full Text Available Abstract: Human Immunodeficiency Virus type 1 (HIV-1 reverse transcriptase is an important target for chemotherapeutic agents against the AIDS disease. 4,5,6,7-Tetrahydro-5-methylimidazo[4,5,1-jk][1,4]benzodiazepin-2(1H-ones (TIBO derivatives are potent non-nucleoside reverse transcriptase inhibitors (NNRTIs. In the present work, quantitative structure-activity relationship (QSAR analysis for a set of 82 TIBO derivatives has been investigated by means of a three-layered neural network (NN. It has been shown that NN can be a potential tool in the investigation of QSAR analysis compared with the models given in the literature. NN gave good statistical results both in fitting and prediction processes (0.861 ≤ r² ≤ 0.928, 0.839 ≤q² ≤ 0.845. The relevant factors controlling the anti-HIV-1 activity of TIBO derivatives have been identified. The results are along the same lines as those of our previous studies on HEPT derivatives and indicate the importance of the hydrophobic parameter in modeling the QSAR for TIBO derivatives.

  4. QSAR modeling and prediction of the endocrine-disrupting potencies of brominated flame retardants.

    Science.gov (United States)

    Papa, Ester; Kovarich, Simona; Gramatica, Paola

    2010-05-17

    In the European Union REACH regulation, the chemicals with particularly harmful behaviors, such as endocrine disruptors (EDs), are subject to authorization, and the identification of safer alternatives to these chemicals is required. In this context, the use of quantitative structure-activity relationships (QSAR) becomes particularly useful to fill the data gap due to the very small number of experimental data available to characterize the environmental and toxicological profiles of new and emerging pollutants with ED behavior such as brominated flame retardants (BFRs). In this study, different QSAR models were developed on different responses of endocrine disruption measured for several BFRs. The multiple linear regression approach was applied to a variety of theoretical molecular descriptors, and the best models, which were identified from all of the possible combinations of the structural variables, were internally validated for their performance using the leave-one-out (Q(LOO)(2) = 73-91%) procedure and scrambling of the responses. External validation was provided, when possible, by splitting the data sets in training and test sets (range of Q(EXT)(2) = 76-90%), which confirmed the predictive ability of the proposed equations. These models, which were developed according to the principles defined by the Organization for Economic Co-operation and Development to improve the regulatory acceptance of QSARs, represent a simple tool for the screening and characterization of BFRs.

  5. QSAR classification models for the prediction of endocrine disrupting activity of brominated flame retardants.

    Science.gov (United States)

    Kovarich, Simona; Papa, Ester; Gramatica, Paola

    2011-06-15

    The identification of potential endocrine disrupting (ED) chemicals is an important task for the scientific community due to their diffusion in the environment; the production and use of such compounds will be strictly regulated through the authorization process of the REACH regulation. To overcome the problem of insufficient experimental data, the quantitative structure-activity relationship (QSAR) approach is applied to predict the ED activity of new chemicals. In the present study QSAR classification models are developed, according to the OECD principles, to predict the ED potency for a class of emerging ubiquitary pollutants, viz. brominated flame retardants (BFRs). Different endpoints related to ED activity (i.e. aryl hydrocarbon receptor agonism and antagonism, estrogen receptor agonism and antagonism, androgen and progesterone receptor antagonism, T4-TTR competition, E2SULT inhibition) are modeled using the k-NN classification method. The best models are selected by maximizing the sensitivity and external predictive ability. We propose simple QSARs (based on few descriptors) characterized by internal stability, good predictive power and with a verified applicability domain. These models are simple tools that are applicable to screen BFRs in relation to their ED activity, and also to design safer alternatives, in agreement with the requirements of REACH regulation at the authorization step. Copyright © 2011 Elsevier B.V. All rights reserved.

  6. Modeling the QSAR of ACE-Inhibitory Peptides with ANN and Its Applied Illustration

    Directory of Open Access Journals (Sweden)

    Ronghai He

    2012-01-01

    Full Text Available A quantitative structure-activity relationship (QSAR model of angiotensin-converting enzyme- (ACE- inhibitory peptides was built with an artificial neural network (ANN approach based on structural or activity data of 58 dipeptides (including peptide activity, hydrophilic amino acids content, three-dimensional shape, size, and electrical parameters, the overall correlation coefficient of the predicted versus actual data points is =0.928, and the model was applied in ACE-inhibitory peptides preparation from defatted wheat germ protein (DWGP. According to the QSAR model, the C-terminal of the peptide was found to have principal importance on ACE-inhibitory activity, that is, if the C-terminal is hydrophobic amino acid, the peptide's ACE-inhibitory activity will be high, and proteins which contain abundant hydrophobic amino acids are suitable to produce ACE-inhibitory peptides. According to the model, DWGP is a good protein material to produce ACE-inhibitory peptides because it contains 42.84% of hydrophobic amino acids, and structural information analysis from the QSAR model showed that proteases of Alcalase and Neutrase were suitable candidates for ACE-inhibitory peptides preparation from DWGP. Considering higher DH and similar ACE-inhibitory activity of hydrolysate compared with Neutrase, Alcalase was finally selected through experimental study.

  7. Quantitative structure activity relationship study of anticonvulsant activity of α_substituted acetamido-N-benzylacetamide derivatives

    OpenAIRE

    Usman Abdulfatai; Adamu Uzairu; Sani Uba

    2016-01-01

    To develop the quantitative structure–activity relationship (QSAR) for predicting the anticonvulsant activity of α_substituted acetamido-N-benzylacetamide derivatives. Density Functional Theory (B3LYP/6-31G*) quantum chemical calculation method was used to find the optimized geometry of the studied molecules. Nine types of molecular descriptors were used to derive a quantitative relation between anticonvulsant activity and structural properties. The relevant molecular descriptors were selecte...

  8. Descriptive mining for the QSAR problem

    Directory of Open Access Journals (Sweden)

    Lucian GEORGESCU

    2005-12-01

    Full Text Available There are several approaches in trying to solve the Quantitative Structure-Activity (QSAR problem. These approaches are based either on statistical methods or on predictive data mining using neural networks. Among the statistical methods, one should consider regression analysis, pattern recognition (such as cluster analysis, factor analysis and principal components analysis or partial least squares. These approaches have a low explanatory capability or non at all. This paper attempts to establish a new approach in solving QSSAR problems using descriptive data mining. This way, the relationship between the chemical properties and the activity of a substance would be comprehensibly modeled.

  9. Prediction of Halocarbon Toxicity from Structure: A Hierarchical QSAR Approach

    Energy Technology Data Exchange (ETDEWEB)

    Gute, B D; Balasubramanian, K; Geiss, K; Basak, S C

    2003-04-11

    Mathematical structural invariants and quantum theoretical descriptors have been used extensively in quantitative structure-activity relationships (QSARs) for the estimation of pharmaceutical activities, biological properties, physicochemical properties, and the toxicities of chemicals. Recently our research team has explored the relative importance of various levels of chemodescriptors, i.e., topostructural, topochemical, geometrical, and quantum theoretical descriptors, in property estimation. This study examines the contribution of chemodescriptors ranging from topostructural to quantum theoretic calculations up to the Gaussian STO-3G level in the prediction of the toxicity of a set of twenty halocarbons. We also report the results of experimental cell-level toxicity studies on these twenty halocarbons to validate our models.

  10. Alternative methods for estimating common descriptors for QSAR studies of dyes and fluorescent probes using molecular modeling software. 2. Correlations between log P and the hydrophilic/lipophilic index, and new methods for estimating degrees of amphiphilicity.

    Science.gov (United States)

    Dapson, Richard W; Horobin, Richard W

    2013-11-01

    The log P descriptor, despite its usefulness, can be difficult to use, especially for researchers lacking skills in physical chemistry. Moreover this classic measure has been determined in numerous ways, which can result in inconsistant estimates of log P values, especially for relatively complex molecules such as fluorescent probes. Novel measures of hydrophilicity/lipophilicity (the Hydrophilic/Lipophilic Index, HLI) and amphiphilicity (hydrophilic/lipophilic indices for the head group and tail, HLIT and HLIHG, respectively) therefore have been devised. We compare these descriptors with measures based on log P, the standard method for quantitative structure activity relationships (QSAR) studies. HLI can be determined using widely available molecular modeling software, coupled with simple arithmetic calculations. It is based on partial atomic charges and is intended to be a stand-alone measure of hydrophilicity/lipophilicity. Given the wide application of log P, however, we investigated the correlation between HLI and log P using a test set of 56 fluorescent probes of widely different physicochemical character. Overall correlation was poor; however, correlation of HLI and log P for probes of narrowly specified charge types, i.e., non-ionic compounds, anions, conjugated cations, or zwitterions, was excellent. Values for probes with additional nonconjugated quaternary cations, however, were less well correlated. The newly devised HLI can be divided into domain-specific descriptors, HLIT and HLIHG in amphiphilic probes. Determinations of amphiphilicity, made independently by the authors using their respective methods, showed excellent agreement. Quantifying amphiphilicity from partial log P values of the head group (head group hydrophilicity; HGH) and tail (amphiphilicity index; AI) has proved useful for understanding fluorescent probe action. The same limitations of log P apply to HGH and AI, however. The novel descriptors, HLIT and HLIHG, offer analogous advantages

  11. On various metrics used for validation of predictive QSAR models with applications in virtual screening and focused library design.

    Science.gov (United States)

    Roy, Kunal; Mitra, Indrani

    2011-07-01

    Quantitative structure-activity relationships (QSARs) have important applications in drug discovery research, environmental fate modeling, property prediction, etc. Validation has been recognized as a very important step for QSAR model development. As one of the important objectives of QSAR modeling is to predict activity/property/toxicity of new chemicals falling within the domain of applicability of the developed models and QSARs are being used for regulatory decisions, checking reliability of the models and confidence of their predictions is a very important aspect, which can be judged during the validation process. One prime application of a statistically significant QSAR model is virtual screening for molecules with improved potency based on the pharmacophoric features and the descriptors appearing in the QSAR model. Validated QSAR models may also be utilized for design of focused libraries which may be subsequently screened for the selection of hits. The present review focuses on various metrics used for validation of predictive QSAR models together with an overview of the application of QSAR models in the fields of virtual screening and focused library design for diverse series of compounds with citation of some recent examples.

  12. Combined 3D-QSAR and molecular docking study on 7,8-dialkyl-1,3-diaminopyrrolo-[3,2-f] Quinazoline series compounds to understand the binding mechanism of DHFR inhibitors

    Science.gov (United States)

    Aouidate, Adnane; Ghaleb, Adib; Ghamali, Mounir; Chtita, Samir; Choukrad, M'barek; Sbai, Abdelouahid; Bouachrine, Mohammed; Lakhlifi, Tahar

    2017-07-01

    A series of nineteen DHFR inhibitors was studied based on the combination of two computational techniques namely, three-dimensional quantitative structure activity relationship (3D-QSAR) and molecular docking. The comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) were developed using 19 molecules having pIC50 ranging from 9.244 to 5.839. The best CoMFA and CoMSIA models show conventional determination coefficients R2 of 0.96 and 0.93 as well as the Leave One Out cross-validation determination coefficients Q2 of 0.64 and 0.72, respectively. The predictive ability of those models was evaluated by the external validation using a test set of five compounds with predicted determination coefficients R2test of 0.92 and 0.94, respectively. The binding mode between this kind of compounds and the DHFR enzyme in addition to the key amino acid residues were explored by molecular docking simulation. Contour maps and molecular docking identified that the R1 and R2 natures at the pyrazole moiety are the important features for the optimization of the binding affinity to the DHFR receptor. According to the good concordance between the CoMFA/CoMSIA contour maps and docking results, the obtained information was explored to design novel molecules.

  13. QSAR modelling using combined simple competitive learning networks and RBF neural networks.

    Science.gov (United States)

    Sheikhpour, R; Sarram, M A; Rezaeian, M; Sheikhpour, E

    2018-04-01

    The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.

  14. QSAR study and the hydrolysis activity prediction of three alkaline lipases from different lipase-producing microorganisms

    Directory of Open Access Journals (Sweden)

    Wang Haikuan

    2012-09-01

    Full Text Available Abstract The hydrolysis activities of three alkaline lipases, L-A1, L-A2 and L-A3 secreted by different lipase-producing microorganisms isolated from the Bay of Bohai, P. R. China were characterized with 16 kinds of esters. It was found that all the lipases have the ability to catalyze the hydrolysis of the glycerides, methyl esters, ethyl esters, especially for triglycerides, which shows that they have broad substrate spectra, and this property is very important for them to be used in detergent industry. Three QSAR models were built for L-A1, L-A2 and L-A3 respectively with GFA using Discovery studio 2.1. The models equations 1, 2 and 3 can explain 95.80%, 97.45% and 97.09% of the variances (R2adj respectively while they could predict 95.44%, 89.61% and 93.41% of the variances (R2cv respectively. With these models the hydrolysis activities of these lipases to mixed esters were predicted and the result showed that the predicted values are in good agreement with the measured values, which indicates that this method can be used as a simple tool to predict the lipase activities for single or mixed esters.

  15. Considerations and recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction

    Science.gov (United States)

    Li, Haiyan; Sun, Jin; Fan, Xiaowen; Sui, Xiaofan; Zhang, Lan; Wang, Yongjun; He, Zhonggui

    2008-11-01

    Quantitative structure-activity relationships (QSAR) methods are urgently needed for predicting ADME/T (absorption, distribution, metabolism, excretion and toxicity) properties to select lead compounds for optimization at the early stage of drug discovery, and to screen drug candidates for clinical trials. Use of suitable QSAR models ultimately results in lesser time-cost and lower attrition rate during drug discovery and development. In the case of ADME/T parameters, drug metabolism is a key determinant of metabolic stability, drug-drug interactions, and drug toxicity. QSAR models for predicting drug metabolism have undergone significant advances recently. However, most of the models used lack sufficient interpretability and offer poor predictability for novel drugs. In this review, we describe some considerations to be taken into account by QSAR for modeling drug metabolism, such as the accuracy/consistency of the entire data set, representation and diversity of the training and test sets, and variable selection. We also describe some novel statistical techniques (ensemble methods, multivariate adaptive regression splines and graph machines), which are not yet used frequently to develop QSAR models for drug metabolism. Subsequently, rational recommendations for developing predictable and interpretable QSAR models are made. Finally, the recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction, including in vivo hepatic clearance, in vitro metabolic stability, inhibitors and substrates of cytochrome P450 families, are briefly summarized.

  16. The effect of various atomic partial charge schemes to elucidate consensus activity-correlating molecular regions: a test case of diverse QSAR models.

    Science.gov (United States)

    Kumar, Sivakumar Prasanth; Jha, Prakash C; Jasrai, Yogesh T; Pandya, Himanshu A

    2016-01-01

    The estimation of atomic partial charges of the small molecules to calculate molecular interaction fields (MIFs) is an important process in field-based quantitative structure-activity relationship (QSAR). Several studies showed the influence of partial charge schemes that drastically affects the prediction accuracy of the QSAR model and focused on the selection of appropriate charge models that provide highest cross-validated correlation coefficient ([Formula: see text] or q(2)) to explain the variation in chemical structures against biological endpoints. This study shift this focus in a direction to understand the molecular regions deemed to explain SAR in various charge models and recognize a consensus picture of activity-correlating molecular regions. We selected eleven diverse dataset and developed MIF-based QSAR models using various charge schemes including Gasteiger-Marsili, Del Re, Merck Molecular Force Field, Hückel, Gasteiger-Hückel, and Pullman. The generalized resultant QSAR models were then compared with Open3DQSAR model to interpret the MIF descriptors decisively. We suggest the regions of activity contribution or optimization can be effectively determined by studying various charge-based models to understand SAR precisely.

  17. Synthesis, biological evaluation, QSAR analysis, and molecular docking of chalcone derivatives for antimalarial activity

    Directory of Open Access Journals (Sweden)

    Jufrizal Syahri

    2017-01-01

    Full Text Available Objective: To synthesize chalcone derivatives and investigate their antimalarial activity toward chloroquine-sensitive Plasmodium falciparum 3D7 (Pf3D7 strain; to develop quantitative structureactivity relationships (QSAR model to estimate IC50 values for biological activity of antimalarial and compared to experimental measurement; and to determine the binding interactions of the most active compounds with targeting P. falciparum dihydrofolate reductase-thymidylate synthase using molecular docking simulation. Methods: Seven chalcone derivatives have been synthesized from substituted acetophenone and substituted benzaldehyde in ethanol with the presence of bases catalysis at reflux condition. The QSAR analysis was conducted by using Gaussian 09 software to predict IC50 value for antimalarial activity. The in vitro test was evaluated against the chloroquine-sensitive Pf3D7 strain. Finally, the docking studies were performed with the CDOCKER protocol under the receptor-ligand interaction section in Discovery Studio® 3.1 (Accelrys, Inc., San Diego, USA. Results: Among the synthesized chalcone, a prenylated chalcone 5c and an allylated chalcones 10a showed the best IC 50 values of 1.08 and 1.73 μg/mL respectively against Pf3D7 strain (1.37 and 2.33 μg/mL based on QSAR analysis. Comparison between the prediction of IC50 value generated from the QSAR and the outcome from an in vitro assay showed a similar result as seen from the r2 value (r2 = 0.99. The most active compound 5c was employed in the docking simulation to determine the potential binding interactions with active sites of P. falciparum dihydrofolate reductase-thymidylate synthase (protein data bank ID: 1J3I. The docking simulation study showed 5c bind well with Ala16, Ser108, Ile164, Trp48, and Phe58 which are the crucial interactions that could possibly interrupt the sequential catalysis reactions in the thymidylate cycle and subsequently prevent deoxythymidine monophosphate production

  18. A 3D-QSAR-driven approach to binding mode and affinity prediction

    DEFF Research Database (Denmark)

    Tosco, Paolo; Balle, Thomas

    2012-01-01

    A method for predicting the binding mode of a series of ligands is proposed. The procedure relies on three-dimensional quantitative structure-activity relationships (3D-QSAR) and does not require structural knowledge of the binding site. Candidate alignments are automatically built and ranked acc...... according to a consensus scoring function. 3D-QSAR analysis based on the selected binding mode enables affinity prediction of new drug candidates having less than 10 rotatable bonds....

  19. Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action

    Science.gov (United States)

    Papamokos, George; Silins, Ilona

    2016-01-01

    There is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens. PMID:27625608

  20. Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action.

    Science.gov (United States)

    Papamokos, George; Silins, Ilona

    2016-01-01

    There is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens.

  1. QSAR of phytochemicals for the design of better drugs.

    Science.gov (United States)

    Kar, Supratik; Roy, Kunal

    2012-10-01

    Phytochemicals have been the single most prolific source of leads for the development of new drug entities from the dawn of the drug discovery. They cover a wide range of therapeutic indications with a great diversity of chemical structures. The research fraternity still believes in exploring the phytochemicals for new drug discovery. Application of molecular biological techniques has increased the availability of novel compounds that can be conveniently isolated from natural sources. Combinatorial chemistry approaches are being applied based on phytochemical scaffolds to create screening libraries that closely resemble drug-like compounds. In silico techniques like quantitative structure-activity relationships (QSAR), pharmacophore and virtual screening are playing crucial and rate accelerating steps for the better drug design in modern era. QSAR models of different classes of phytochemicals covering different therapeutic areas are thoroughly discussed in the review. Further, the authors have enlisted all the available phytochemical databases for the convenience of researchers working in the area. This review justifies the need to develop more QSAR models for the design of better drugs from phytochemicals. Technical drawbacks associated with phytochemical research have been lessened, and there are better opportunities to explore the biological activity of previously inaccessible sources of phytochemicals although there is still the need to reduce the time and cost involvement in such exercise. The future possibilities for the integration of ethnopharmacology with QSAR, place us at an exciting stage that will allow us to explore plant sources worldwide and design better drugs.

  2. A QSAR/QSTR Study on the Environmental Health Impact by the Rocket Fuel 1,1-Dimethyl Hydrazine and its Transformation Products.

    Science.gov (United States)

    Carlsen, Lars; Kenessov, Bulat N; Batyrbekova, Svetlana Ye

    2008-07-18

    QSAR/QSTR modelling constitutes an attractive approach to preliminary assessment of the impact on environmental health by a primary pollutant and the suite of transformation products that may be persistent in and toxic to the environment. The present paper studies the impact on environmental health by residuals of the rocket fuel 1,1-dimethyl hydrazine (heptyl) and its transformation products. The transformation products, comprising a variety of nitrogen containing compounds are suggested all to possess a significant migration potential. In all cases the compounds were found being rapidly biodegradable. However, unexpected low microbial activity may cause significant changes. None of the studied compounds appear to be bioaccumulating.Apart from substances with an intact hydrazine structure or hydrazone structure the transformation products in general display rather low environmental toxicities. Thus, it is concluded that apparently further attention should be given to tri- and tetramethyl hydrazine and 1-formyl 2,2-dimethyl hydrazine as well as to the hydrazones of formaldehyde and acetaldehyde as these five compounds may contribute to the overall environmental toxicity of residual rocket fuel and its transformation products.

  3. Towards understanding the mechanism of action of antibacterial N-alkyl-3-hydroxypyridinium salts: Biological activities, molecular modeling and QSAR studies.

    Science.gov (United States)

    Dolezal, Rafael; Soukup, Ondrej; Malinak, David; Savedra, Ranylson M L; Marek, Jan; Dolezalova, Marie; Pasdiorova, Marketa; Salajkova, Sarka; Korabecny, Jan; Honegr, Jan; Ramalho, Teodorico C; Kuca, Kamil

    2016-10-04

    In this study, we have carried out a combined experimental and computational investigation to elucidate several bred-in-the-bone ideas standing out in rational design of novel cationic surfactants as antibacterial agents. Five 3-hydroxypyridinium salts differing in the length of N-alkyl side chain have been synthesized, analyzed by high performance liquid chromatography, tested for in vitro activity against a panel of pathogenic bacterial and fungal strains, computationally modeled in water by a SCRF B3LYP/6-311++G(d,p) method, and evaluated by a systematic QSAR analysis. Given the results of this work, the hypothesis suggesting that higher positive charge of the quaternary nitrogen should increase antimicrobial efficacy can be rejected since 3-hydroxyl group does increase the positive charge on the nitrogen but, simultaneously, it significantly derogates the antimicrobial activity by lowering the lipophilicity and by escalating the desolvation energy of the compounds in comparison with non-hydroxylated analogues. Herein, the majority of the prepared 3-hydroxylated substances showed notably lower potency than the parent pyridinium structures, although compound 8 with C12 alkyl chain proved a distinctly better antimicrobial activity in submicromolar range. Focusing on this anomaly, we have made an effort to reveal the reason of the observed activity through a molecular dynamics simulation of the interaction between the bacterial membrane and compound 8 in GROMACS software. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

  4. 4-(1-Aryl-5-chloro-2-oxo-1,2-dihydro-indol-3-ylideneamino-N-substituted benzene sulfonamides: Synthesis, antimicrobial, anticancer evaluation and QSAR studies

    Directory of Open Access Journals (Sweden)

    Mahesh Kumar

    2014-09-01

    Full Text Available A series of 4-(1-aryl-5-chloro-2-oxo-1,2-dihydro-indol-3-ylideneamino-N-substituted benzenesulfonamide derivatives (1–20 was synthesized and evaluated for its in vitro antimicrobial and anticancer activities. Antimicrobial results indicated that compounds N-(4-(1-benzoyl-5-chloro-2-oxoindolin-3-ylideneamino phenylsulfonyl-4-isopropoxy benzamide (9 and N-(4-(5-chloro-1-(2-chlorobenzoyl-2-oxoindolin-3-ylideneamino phenylsulfonyl-4-isopropoxybenzamide (19 were found to be the most effective ones. The anticancer results indicated that almost all the synthesized compounds were more active than the standard drug carboplatin but less active than the standard drug 5-fluorouracil (5-FU against both the cell lines (HCT116 and RAW 264.7. 4-(1-Benzoyl-5-chloro-2-oxoindolin-3-ylideneamino-N-(pyrimidin-2-yl benzenesulfonamide (3 was found to be most potent and exhibited selectivity toward HCT 116. QSAR studies indicated that antimicrobial activity of isatin derivatives against different microbial strains was governed by lipophilic parameter, log P and topological parameters valance zero and third order molecular connectivity indices (0χv and 3χv.

  5. Predicting Drug-induced Hepatotoxicity Using QSAR and Toxicogenomics Approaches

    Science.gov (United States)

    Low, Yen; Uehara, Takeki; Minowa, Yohsuke; Yamada, Hiroshi; Ohno, Yasuo; Urushidani, Tetsuro; Sedykh, Alexander; Muratov, Eugene; Fourches, Denis; Zhu, Hao; Rusyn, Ivan; Tropsha, Alexander

    2014-01-01

    Quantitative Structure-Activity Relationship (QSAR) modeling and toxicogenomics are used independently as predictive tools in toxicology. In this study, we evaluated the power of several statistical models for predicting drug hepatotoxicity in rats using different descriptors of drug molecules, namely their chemical descriptors and toxicogenomic profiles. The records were taken from the Toxicogenomics Project rat liver microarray database containing information on 127 drugs (http://toxico.nibio.go.jp/datalist.html). The model endpoint was hepatotoxicity in the rat following 28 days of exposure, established by liver histopathology and serum chemistry. First, we developed multiple conventional QSAR classification models using a comprehensive set of chemical descriptors and several classification methods (k nearest neighbor, support vector machines, random forests, and distance weighted discrimination). With chemical descriptors alone, external predictivity (Correct Classification Rate, CCR) from 5-fold external cross-validation was 61%. Next, the same classification methods were employed to build models using only toxicogenomic data (24h after a single exposure) treated as biological descriptors. The optimized models used only 85 selected toxicogenomic descriptors and had CCR as high as 76%. Finally, hybrid models combining both chemical descriptors and transcripts were developed; their CCRs were between 68 and 77%. Although the accuracy of hybrid models did not exceed that of the models based on toxicogenomic data alone, the use of both chemical and biological descriptors enriched the interpretation of the models. In addition to finding 85 transcripts that were predictive and highly relevant to the mechanisms of drug-induced liver injury, chemical structural alerts for hepatotoxicity were also identified. These results suggest that concurrent exploration of the chemical features and acute treatment-induced changes in transcript levels will both enrich the

  6. QSAR Models for the Prediction of Plasma Protein Binding

    Directory of Open Access Journals (Sweden)

    Zeshan Amin

    2013-02-01

    Full Text Available Introduction: The prediction of plasma protein binding (ppb is of paramount importance in the pharmacokinetics characterization of drugs, as it causes significant changes in volume of distribution, clearance and drug half life. This study utilized Quantitative Structure – Activity Relationships (QSAR for the prediction of plasma protein binding. Methods: Protein binding values for 794 compounds were collated from literature. The data was partitioned into a training set of 662 compounds and an external validation set of 132 compounds. Physicochemical and molecular descriptors were calculated for each compound using ACD labs/logD, MOE (Chemical Computing Group and Symyx QSAR software packages. Several data mining tools were employed for the construction of models. These included stepwise regression analysis, Classification and Regression Trees (CART, Boosted trees and Random Forest. Results: Several predictive models were identified; however, one model in particular produced significantly superior prediction accuracy for the external validation set as measured using mean absolute error and correlation coefficient. The selected model was a boosted regression tree model which had the mean absolute error for training set of 13.25 and for validation set of 14.96. Conclusion: Plasma protein binding can be modeled using simple regression trees or multiple linear regressions with reasonable model accuracies. These interpretable models were able to identify the governing molecular factors for a high ppb that included hydrophobicity, van der Waals surface area parameters, and aromaticity. On the other hand, the more complicated ensemble method of boosted regression trees produced the most accurate ppb estimations for the external validation set.

  7. QSAR study of the non-peptidic inhibitors of procollagen C-proteinase based on Multiple linear regression, principle component regression, and partial least squares

    Directory of Open Access Journals (Sweden)

    Ardeshir Khazaei

    2017-09-01

    Full Text Available The quantitative structure–activity relationship (QSAR analyses were carried out in a series of novel sulfonamide derivatives as the procollagen C-proteinase inhibitors for treatment of fibrotic conditions. Sphere exclusion method was used to classify data set into categories of train and test set at different radii ranging from 0.9 to 0.5. Multiple linear regression (MLR, principal component regression (PCR and partial least squares (PLS were used as the regression methods and stepwise, Genetic algorithm (GA, and simulated annealing (SA were used as the feature selection methods. Three of the statistically best significant models were chosen from the results for discussion. Model 1 was obtained by MLR–SA methodology at a radius of 1.6. This model with a coefficient of determination (r2 = 0.71 can well predict the real inhibitor activities. Cross-validated q2 of this model, 0.64, indicates good internal predictive power of the model. External validation of the model (pred_r2 = 0.85 showed that the model can well predict activity of novel PCP inhibitors. The model 2 which developed using PLS–SW explains 72% (r2 = 0.72 of the total variance in the training set as well as it has internal (q2 and external (pred_r2 predictive ability of ∼67% and ∼71% respectively. The last developed model by PCR–SA has a correlation coefficient (r2 of 0.68 which can explains 68% of the variance in the observed activity values. In this case internal and external validations are 0.61 and 0.75, respectively. Alignment Independent (AI and atomic valence connectivity index (chiv have the greatest effect on the biological activities. Developed models can be useful in designing and synthesis of effective and optimized novel PCP inhibitors which can be used for treatment of fibrotic conditions.

  8. Quantitative structure–activity relationship based modeling of substituted indole Schiff bases as inhibitor of COX-2

    OpenAIRE

    Dwivedi, Amrita; Singh, Ajeet; Srivastava, A. K.

    2016-01-01

    We have performed the quantitative structure activity relationship (QSAR) study for N-1 and C-3 substituted indole shiff bases to understand the structural features that influence the inhibitory activity toward the cyclooxygenase-2 (COX-2) enzyme. The calculated QSAR results revealed that the drug activity could be modeled by using molecular connectivity indices (0χ, 1χ, 2χ), wiener index (W) and mean wiener index (WA) parameters. The predictive ability of models was cross validated by evalua...

  9. Phenylpropiophenone derivatives as potential anticancer agents: synthesis, biological evaluation and quantitative structure-activity relationship study.

    Science.gov (United States)

    Ivković, Branka M; Nikolic, Katarina; Ilić, Bojana B; Žižak, Željko S; Novaković, Radmila B; Čudina, Olivera A; Vladimirov, Sote M

    2013-05-01

    Series of twelve chalcone and propafenone derivatives has been synthesized and evaluated for anticancer activities against HeLa, Fem-X, PC-3, MCF-7, LS174 and K562 cell lines. The 2D-QSAR and 3D-QSAR studies were performed for all compounds with cytotoxic activities against each cancer cell line. Partial least squares (PLS) regression has been applied for selection of the most relevant molecular descriptors and QSAR models building. Predictive potentials of the created 2D-QSAR and 3D-QSAR models for each cell line were compared, by use of leave-one-out cross-validation and external validation, and optimal QSAR models for each cancer cell line were selected. The QSAR studies have selected the most significant molecular descriptors and pharmacophores of the chalcone and propafenone derivatives and proposed structures of novel chalcone and propafenone derivatives with enhanced anticancer activity on the HeLa, Fem-X, PC-3, MCF-7, LS174 and K562 cells. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

  10. A combined Fisher and Laplacian score for feature selection in QSAR based drug design using compounds with known and unknown activities

    Science.gov (United States)

    Valizade Hasanloei, Mohammad Amin; Sheikhpour, Razieh; Sarram, Mehdi Agha; Sheikhpour, Elnaz; Sharifi, Hamdollah

    2017-12-01

    Quantitative structure-activity relationship (QSAR) is an effective computational technique for drug design that relates the chemical structures of compounds to their biological activities. Feature selection is an important step in QSAR based drug design to select the most relevant descriptors. One of the most popular feature selection methods for classification problems is Fisher score which aim is to minimize the within-class distance and maximize the between-class distance. In this study, the properties of Fisher criterion were extended for QSAR models to define the new distance metrics based on the continuous activity values of compounds with known activities. Then, a semi-supervised feature selection method was proposed based on the combination of Fisher and Laplacian criteria which exploits both compounds with known and unknown activities to select the relevant descriptors. To demonstrate the efficiency of the proposed semi-supervised feature selection method in selecting the relevant descriptors, we applied the method and other feature selection methods on three QSAR data sets such as serine/threonine-protein kinase PLK3 inhibitors, ROCK inhibitors and phenol compounds. The results demonstrated that the QSAR models built on the selected descriptors by the proposed semi-supervised method have better performance than other models. This indicates the efficiency of the proposed method in selecting the relevant descriptors using the compounds with known and unknown activities. The results of this study showed that the compounds with known and unknown activities can be helpful to improve the performance of the combined Fisher and Laplacian based feature selection methods.

  11. An integrated QSAR-PBK/D modelling approach for predicting detoxification and DNA adduct formation of 18 acyclic food-borne α,β-unsaturated aldehydes

    Energy Technology Data Exchange (ETDEWEB)

    Kiwamoto, R., E-mail: reiko.kiwamoto@wur.nl; Spenkelink, A.; Rietjens, I.M.C.M.; Punt, A.

    2015-01-01

    Acyclic α,β-unsaturated aldehydes present in food raise a concern because the α,β-unsaturated aldehyde moiety is considered a structural alert for genotoxicity. However, controversy remains on whether in vivo at realistic dietary exposure DNA adduct formation is significant. The aim of the present study was to develop physiologically based kinetic/dynamic (PBK/D) models to examine dose-dependent detoxification and DNA adduct formation of a group of 18 food-borne acyclic α,β-unsaturated aldehydes without 2- or 3-alkylation, and with no more than one conjugated double bond. Parameters for the PBK/D models were obtained using quantitative structure–activity relationships (QSARs) defined with a training set of six selected aldehydes. Using the QSARs, PBK/D models for the other 12 aldehydes were defined. Results revealed that DNA adduct formation in the liver increases with decreasing bulkiness of the molecule especially due to less efficient detoxification. 2-Propenal (acrolein) was identified to induce the highest DNA adduct levels. At realistic dietary intake, the predicted DNA adduct levels for all aldehydes were two orders of magnitude lower than endogenous background levels observed in disease free human liver, suggesting that for all 18 aldehydes DNA adduct formation is negligible at the relevant levels of dietary intake. The present study provides a proof of principle for the use of QSAR-based PBK/D modelling to facilitate group evaluations and read-across in risk assessment. - Highlights: • Physiologically based in silico models were made for 18 α,β-unsaturated aldehydes. • Kinetic parameters were determined by in vitro incubations and a QSAR approach. • DNA adduct formation was negligible at levels relevant for dietary intake. • The use of QSAR-based PBK/D modelling facilitates group evaluations and read-across.

  12. Quantitative Structure Activity Relationship of Cinnamaldehyde Compounds against Wood-Decaying Fungi

    Directory of Open Access Journals (Sweden)

    Dongmei Yang

    2016-11-01

    Full Text Available Cinnamaldehyde, of the genius Cinnamomum, is a major constituent of the bark of the cinnamon tree and possesses broad-spectrum antimicrobial activity. In this study, we used best multiple linear regression (BMLR to develop quantitative structure activity relationship (QSAR models for cinnamaldehyde derivatives against wood-decaying fungi Trametes versicolor and Gloeophyllun trabeum. Based on the two optimal QSAR models, we then designed and synthesized two novel cinnamaldehyde compounds. The QSAR models exhibited good correlation coefficients: R2Tv = 0.910 for Trametes versicolor and R2Gt = 0.926 for Gloeophyllun trabeum. Small errors between the experimental and calculated values of two designed compounds indicated that these two QSAR models have strong predictability and stability.

  13. Quantitative structure-activity relationship for prediction of the toxicity of phenols on Photobacterium phosphoreum.

    Science.gov (United States)

    Li, Xiaolin; Wang, Zunyao; Liu, Hongling; Yu, Hongxia

    2012-07-01

    Quantitative structure-activity relationships (QSAR) is an alternative to experimental toxicity testing and recommended by environmental protection agencies. In this background, an accurate and reliable QSAR model of 18 phenols for their toxicity to Photobacterium phosphoreum was developed using mechanistically interpretable molecular structural descriptors. The QSAR model was developed by stepwise multiple linear regression and the reliability of the model was evaluated by internal and external validation. The cross-validated correlation coefficient (q (2)) was 0.7021, indicating good predictive ability for the toxicity of these phenols. The QSAR model suggests that the toxicity of the studied compounds mainly depends on the logarithm of octanol/water partition coefficient, dipole moment and the most negative atomic charge.

  14. Quantitative Structure Activity Relationship of Cinnamaldehyde Compounds against Wood-Decaying Fungi.

    Science.gov (United States)

    Yang, Dongmei; Wang, Hui; Yuan, Haijian; Li, Shujun

    2016-11-17

    Cinnamaldehyde, of the genius Cinnamomum, is a major constituent of the bark of the cinnamon tree and possesses broad-spectrum antimicrobial activity. In this study, we used best multiple linear regression (BMLR) to develop quantitative structure activity relationship (QSAR) models for cinnamaldehyde derivatives against wood-decaying fungi Trametes versicolor and Gloeophyllun trabeum. Based on the two optimal QSAR models, we then designed and synthesized two novel cinnamaldehyde compounds. The QSAR models exhibited good correlation coefficients: R²Tv = 0.910 for Trametes versicolor and R²Gt = 0.926 for Gloeophyllun trabeum. Small errors between the experimental and calculated values of two designed compounds indicated that these two QSAR models have strong predictability and stability.

  15. Characterization of β3-adrenergic receptor: determination of pharmacophore and 3D QSAR model for β3 adrenergic receptor agonism

    Science.gov (United States)

    Prathipati, Philip; Saxena, Anil K.

    2005-02-01

    The β3-adrenoreceptor (β3-AR) has been shown to mediate various pharmacological and physiological effects such as lipolysis, thermogenesis, and intestinal smooth muscle relaxation. It also plays an important role in glucose homeostasis and energy balance. Molecular modeling studies were undertaken to develop predictive pharmacophoric hypothesis and 3D-QSAR model, which may explain variations in β3-AR agonistic activity in terms of chemical features and physicochemical properties. The two softwares, CATALYST for pharmacophoric alignment and APEX-3D for 3D-QSAR modeling were used to establish the structure activity relationships for β3-AR agonistic activity. Among the several statistically significant models, the selection of the best pharmacophore and 3D-QSAR model was based on its ability to estimate the activity of external test sets of similar and different structural types along with the reasonable consistency of the model with the limited information of the active site of β3-AR. The final 3D-QSAR model was derived using the pharmacophoric alignments from the hypothesis which consisted of four chemical features: basic or positive ionizable feature on the nitrogen of the aryloxypropylamino group, two ring aromatic features corresponding to the phenyl ring of the phenoxide and the benzenesulphonamido groups and a hydrogen-bond donor (HBD) in the vicinity of the nitrogen atom of the benzenesulphonamido group with the most active molecule mapping in an energetically favorable extended conformation. This hypothesis was in agreement with the site directed mutagenesis studies on human β3-AR and correlated well the observed and estimated activity both in, training and both the external test sets. It also mapped reasonably well to six β3-AR agonists of different structural classes under clinical development and thus this hypothesis may have a universal applicability in providing a powerful template for virtual screening and also for designing new chemical entities

  16. Rational drug design for anti-cancer chemotherapy: multi-target QSAR models for the in silico discovery of anti-colorectal cancer agents.

    Science.gov (United States)

    Speck-Planche, Alejandro; Kleandrova, Valeria V; Luan, Feng; Cordeiro, M Natália D S

    2012-08-01

    The discovery of new and more potent anti-cancer agents constitutes one of the most active fields of research in chemotherapy. Colorectal cancer (CRC) is one of the most studied cancers because of its high prevalence and number of deaths. In the current pharmaceutical design of more efficient anti-CRC drugs, the use of methodologies based on Chemoinformatics has played a decisive role, including Quantitative-Structure-Activity Relationship (QSAR) techniques. However, until now, there is no methodology able to predict anti-CRC activity of compounds against more than one CRC cell line, which should constitute the principal goal. In an attempt to overcome this problem we develop here the first multi-target (mt) approach for the virtual screening and rational in silico discovery of anti-CRC agents against ten cell lines. Here, two mt-QSAR classification models were constructed using a large and heterogeneous database of compounds. The first model was based on linear discriminant analysis (mt-QSAR-LDA) employing fragment-based descriptors while the second model was obtained using artificial neural networks (mt-QSAR-ANN) with global 2D descriptors. Both models correctly classified more than 90% of active and inactive compounds in training and prediction sets. Some fragments were extracted from the molecules and their contributions to anti-CRC activity were calculated using mt-QSAR-LDA model. Several fragments were identified as potential substructural features responsible for the anti-CRC activity and new molecules designed from those fragments with positive contributions were suggested and correctly predicted by the two models as possible potent and versatile anti-CRC agents. Copyright © 2012 Elsevier Ltd. All rights reserved.

  17. Investigations and design of pyridine-2-carboxylic acid thiazol-2-ylamide analogs as methionine aminopeptidase inhibitors using 3D-QSAR and molecular docking

    DEFF Research Database (Denmark)

    Hauser, Alexander Sebastian

    2014-01-01

    -dimensional quantitative structure–activity relationship (3D-QSAR) studies were carried out on a series of pyridine-2-carboxylic acid thiazol-2-ylamide-based MetAP inhibitors using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. The models were....... These inhibitors were docked into MetAP active site. The CoMFA and CoMSIA field contour maps correlate well with the structural characteristics of the binding pocket of MetAP active site. Using the knowledge of structure–activity relationship and receptor–ligand interactions from 3D-QSAR model and the docked...... complexes, four new pyridine-2-carboxylic acid thiazol-2-ylamide analogs were designed. These analogs exhibit significantly better predicted activity than the reported molecules. The present work has implications for the development of novel antibiotics as potent MetAP inhibitors....

  18. Quantum chemical parameters in QSAR: what do I use when?

    Science.gov (United States)

    Hickey, James P.; Ostrander, Gary K.

    1996-01-01

    This chapter provides a brief overview of the numerous quantum chemical parameters that have been/are currently being used in quantitative structure activity relationships (QSAR), along with a representative bibliography. The parameters will be grouped according to their mechanistic interpretations, and representative biological and physical chemical applications will be mentioned. Parmater computation methods and the appropriate software are highlighted, as are sources for software.

  19. Molecular modeling studies on series of Btk inhibitors using docking, structure-based 3D-QSAR and molecular dynamics simulation: a combined approach.

    Science.gov (United States)

    Balasubramanian, Pavithra K; Balupuri, Anand; Cho, Seung Joo

    2016-03-01

    Bruton tyrosine kinase (Btk) is a non-receptor tyrosine kinase. It is a crucial component in BCR pathway and expressed only in hematopoietic cells except T cells and Natural killer cells. BTK is a promising target because of its involvement in signaling pathways and B cell diseases such as autoimmune disorders and lymphoma. In this work, a combined molecular modeling study of molecular docking, 3D-QSAR and molecular dynamic (MD) simulation were performed on a series of 2,5-diaminopyrimidine compounds as inhibitors targeting Btk kinase to understand the interaction and key residues involved in the inhibition. A structure based CoMFA (q (2) = 0.675, NOC = 5, r (2) = 0.961) and COMSIA (q (2) = 0.704, NOC = 6, r (2) = 0.962) models were developed from the conformation obtained by docking. The developed models were subjected to various validation techniques such as leave-five-out, external test set, bootstrapping, progressive sampling and rm (2) metrics and found to have a good predictive ability in both internal and external validation. Our docking results showed the important residues that interacts in the active site residues in inhibition of Btk kinase. Furthermore, molecular dynamics simulation was employed to study the stability of the docked conformation and to investigate the binding interactions in detail. The MD simulation analyses identified several important hydrogen bonds with Btk, including the gatekeeper residue Thr474 and Met477 at the hinge region. Hydrogen bond with active site residues Leu408 and Arg525 were also recognized. A good correlation between the MD results, docking studies and the contour map analysis are observed. This indicates that the developed models are reliable. Our results from this study can provide insights in the designing and development of more potent Btk kinase inhibitors.

  20. Pharmacophore generation, atom-based 3D-QSAR, molecular docking and molecular dynamics simulation studies on benzamide analogues as FtsZ inhibitors.

    Science.gov (United States)

    Tripathy, Swayansiddha; Azam, Mohammed Afzal; Jupudi, Srikanth; Sahu, Susanta Kumar

    2017-10-11

    FtsZ is an appealing target for the design of antimicrobial agent that can be used to defeat the multidrug-resistant bacterial pathogens. Pharmacophore modelling, molecular docking and molecular dynamics (MD) simulation studies were performed on a series of three-substituted benzamide derivatives. In the present study a five-featured pharmacophore model with one hydrogen bond acceptors, one hydrogen bond donors, one hydrophobic and two aromatic rings was developed using 97 molecules having MIC values ranging from .07 to 957 μM. A statistically significant 3D-QSAR model was obtained using this pharmacophore hypothesis with a good correlation coefficient (R(2) = .8319), cross validated coefficient (Q(2) = .6213) and a high Fisher ratio (F = 103.9) with three component PLS factor. A good correlation between experimental and predicted activity of the training (R(2) = .83) and test set (R(2) = .67) molecules were displayed by ADHRR.1682 model. The generated model was further validated by enrichment studies using the decoy test and MAE-based criteria to measure the efficiency of the model. The docking studies of all selected inhibitors in the active site of FtsZ protein showed crucial hydrogen bond interactions with Val 207, Asn 263, Leu 209, Gly 205 and Asn-299 residues. The binding free energies of these inhibitors were calculated by the molecular mechanics/generalized born surface area VSGB 2.0 method. Finally, a 15 ns MD simulation was done to confirm the stability of the 4DXD-ligand complex. On a wider scope, the prospect of present work provides insight in designing molecules with better selective FtsZ inhibitory potential.

  1. Towards cheminformatics-based estimation of drug therapeutic index: Predicting the protective index of anticonvulsants using a new quantitative structure-index relationship approach.

    Science.gov (United States)

    Chen, Shangying; Zhang, Peng; Liu, Xin; Qin, Chu; Tao, Lin; Zhang, Cheng; Yang, Sheng Yong; Chen, Yu Zong; Chui, Wai Keung

    2016-06-01

    The overall efficacy and safety profile of a new drug is partially evaluated by the therapeutic index in clinical studies and by the protective index (PI) in preclinical studies. In-silico predictive methods may facilitate the assessment of these indicators. Although QSAR and QSTR models can be used for predicting PI, their predictive capability has not been evaluated. To test this capability, we developed QSAR and QSTR models for predicting the activity and toxicity of anticonvulsants at accuracy levels above the literature-reported threshold (LT) of good QSAR models as tested by both the internal 5-fold cross validation and external validation method. These models showed significantly compromised PI predictive capability due to the cumulative errors of the QSAR and QSTR models. Therefore, in this investigation a new quantitative structure-index relationship (QSIR) model was devised and it showed improved PI predictive capability that superseded the LT of good QSAR models. The QSAR, QSTR and QSIR models were developed using support vector regression (SVR) method with the parameters optimized by using the greedy search method. The molecular descriptors relevant to the prediction of anticonvulsant activities, toxicities and PIs were analyzed by a recursive feature elimination method. The selected molecular descriptors are primarily associated with the drug-like, pharmacological and toxicological features and those used in the published anticonvulsant QSAR and QSTR models. This study suggested that QSIR is useful for estimating the therapeutic index of drug candidates. Copyright © 2016. Published by Elsevier Inc.

  2. Characterization of structure-antioxidant activity relationship of peptides in free radical systems using QSAR models: key sequence positions and their amino acid properties.

    Science.gov (United States)

    Li, Yao-Wang; Li, Bo

    2013-02-07

    related to the antioxidant activity of peptides in the three free radical systems. For peptides in the TEAC database, the relationship between the N-terminal segment (N(2), N(3)) and the activity increased when longer peptides were included, which reflects the likely influence of stericity. This study contributes to the ongoing research on antioxidants in food and its application in medicine. Copyright © 2012 Elsevier Ltd. All rights reserved.

  3. Identification of novel nt-MGAM inhibitors for potential treatment of type 2 diabetes: Virtual screening, atom based 3D-QSAR model, docking analysis and ADME study.

    Science.gov (United States)

    Laoud, Aicha; Ferkous, Fouad; Maccari, Laura; Maccari, Giorgio; Saihi, Youcef; Kraim, Khaireddine

    2017-12-12

    In this study, a virtual screening procedure was applied to identify new potential nt-MGAM inhibitors as a possible medication for type 2 diabetes. To this aim, a series of salacinol analogues were first investigated by docking analysis for their binding to the X-ray structure of the biological target nt-MGAM. Key interactions for ligand binding into the receptor active site were identified which shared common features to those found for other known inhibitors, which strengthen the results of this study. 3D QSAR model was then built and showed to be statistically significant and with a good predictive power for the training (R2 = 0.99, SD = 0.17, F = 555.3 and N = 27) and test set (Q2 = 0.81, Pearson(r) = 0.92, RMSE = 0.52, N = 08). The model was then used to virtually screen the ZINC database with the aim of identifying novel chemical scaffolds as potential nt-MGAM inhibitors. Further, in silico predicted ADME properties were investigated for the most promising molecules. The outcome of this investigation sheds light on the molecular characteristics of the binding of salacinol analogues to nt-MGAM enzyme and identifies new possible inhibitors which have the potential to be developed into drugs, thus significantly contributing to the design and optimization of therapeutic strategies against type 2 diabetes. Copyright © 2017 Elsevier Ltd. All rights reserved.

  4. 3D QSAR studies for the beta-tubulin binding site of microtubule-stabilizing anticancer agents (MSAAs): a pseudoreceptor model for taxanes based on the experimental structure of tubulin.

    Science.gov (United States)

    Maccari, Laura; Manetti, Fabrizio; Corelli, Federico; Botta, Maurizio

    2003-09-01

    The antimitotic agent paclitaxel continues to play an important role in the cancer chemotherapy. However, its inefficacy on certain resistant cells and toxic side effects have led to the search of new taxanes with improved biological activity. By means of a pseudoreceptor modeling approach, we have developed a binding site model for a series of taxanes. It is the first 3D QSAR model derived from the experimentally determined tubulin structure obtained by electron crystallography studies. The model is able to correlate quantitatively the structural properties of the studied compounds with their biological data.

  5. Study of the Differential Activity of Thrombin Inhibitors Using Docking, QSAR, Molecular Dynamics, and MM-GBSA.

    Directory of Open Access Journals (Sweden)

    Karel Mena-Ulecia

    Full Text Available Non-peptidic thrombin inhibitors (TIs; 177 compounds with diverse groups at motifs P1 (such as oxyguanidine, amidinohydrazone, amidine, amidinopiperidine, P2 (such as cyanofluorophenylacetamide, 2-(2-chloro-6-fluorophenylacetamide, and P3 (such as phenylethyl, arylsulfonate groups were studied using molecular modeling to analyze their interactions with S1, S2, and S3 subsites of the thrombin binding site. Firstly, a protocol combining docking and three dimensional quantitative structure-activity relationship was performed. We described the orientations and preferred active conformations of the studied inhibitors, and derived a predictive CoMSIA model including steric, donor hydrogen bond, and acceptor hydrogen bond fields. Secondly, the dynamic behaviors of some selected TIs (compounds 26, 133, 147, 149, 162, and 177 in this manuscript that contain different molecular features and different activities were analyzed by creating the solvated models and using molecular dynamics (MD simulations. We used the conformational structures derived from MD to accomplish binding free energetic calculations using MM-GBSA. With this analysis, we theorized about the effect of van der Waals contacts, electrostatic interactions and solvation in the potency of TIs. In general, the contents reported in this article help to understand the physical and chemical characteristics of thrombin-inhibitor complexes.

  6. 3D-QSAR and Molecular Docking Studies on Derivatives of MK-0457, GSK1070916 and SNS-314 as Inhibitors against Aurora B Kinase

    Directory of Open Access Journals (Sweden)

    Chunzhi Ai

    2010-11-01

    Full Text Available Development of anticancer drugs targeting Aurora B, an important member of the serine/threonine kinases family, has been extensively focused on in recent years. In this work, by applying an integrated computational method, including comparative molecular field analysis (CoMFA, comparative molecular similarity indices analysis (CoMSIA, homology modeling and molecular docking, we investigated the structural determinants of Aurora B inhibitors based on three different series of derivatives of 108 molecules. The resultant optimum 3D-QSAR models exhibited (q2 = 0.605, r2pred = 0.826, (q2 = 0.52, r2pred = 0.798 and (q2 = 0.582, r2pred = 0.971 for MK-0457, GSK1070916 and SNS-314 classes, respectively, and the 3D contour maps generated from these models were analyzed individually. The contour map analysis for the MK-0457 model revealed the relative importance of steric and electrostatic effects for Aurora B inhibition, whereas, the electronegative groups with hydrogen bond donating capacity showed a great impact on the inhibitory activity for the derivatives of GSK1070916. Additionally, the predictive model of the SNS-314 class revealed the great importance of hydrophobic favorable contour, since hydrophobic favorable substituents added to this region bind to a deep and narrow hydrophobic pocket composed of residues that are hydrophobic in nature and thus enhanced the inhibitory activity. Moreover, based on the docking study, a further comparison of the binding modes was accomplished to identify a set of critical residues that play a key role in stabilizing the drug-target interactions. Overall, the high level of consistency between the 3D contour maps and the topographical features of binding sites led to our identification of several key structural requirements for more potency inhibitors. Taken together, the results will serve as a basis for future drug development of inhibitors against Aurora B kinase for various tumors.

  7. QSAR and Molecular Docking Studies of Oxadiazole-Ligated Pyrrole Derivatives as Enoyl-ACP (CoA) Reductase Inhibitors

    National Research Council Canada - National Science Library

    Asgaonkar, Kalyani D; Mote, Ganesh D; Chitre, Trupti S

    2014-01-01

    A quantitative structure-activity relationship model was developed on a series of compounds containing oxadiazole-ligated pyrrole pharmacophore to identify key structural fragments required for anti-tubercular activity. Two-dimensional (2D...

  8. 3D-QSAR studies on CCR2B receptor antagonists: Insight into the structural requirements of (R-3-aminopyrrolidine series of molecules based on CoMFA/CoMSIA models

    Directory of Open Access Journals (Sweden)

    Swetha Gade

    2012-01-01

    Full Text Available Objective: Monocyte chemo attractant protein-1 (MCP-1 is a member of the CC-chemokine family and it selectively recruits leukocytes from the circulation to the site of inflammation through binding with the chemotactic cytokine receptor 2B (CCR2B. The recruitment and activation of selected populations of leukocytes is a key feature in a variety of inflammatory conditions. Thus MCP-1 receptor antagonist represents an attractive target for drug discovery. To understand the structural requirements that will lead to enhanced inhibitory potencies, we have carried out 3D-QSAR (quantitative structure-activity relationship studies on (R-3-aminopyrrolidine series of molecules as CCR2B receptor antagonists. Materials and Methods: Comparative molecular field analysis (CoMFA and comparative molecular similarity indices analysis (CoMSIA were performed on a series of (R-3-aminopyrrolidine derivatives as antagonists of CCR2B receptor with Sybyl 6.7v. Results: We have derived statistically significant model from 37 molecules and validated it against an external test set of 13 compounds. The CoMFA model yielded a leave one out r 2 (r 2 loo of 0.847, non-cross-validated r 2 (r 2 ncv of 0.977, F value of 267.930, and bootstrapped r 2 (r 2 bs of 0.988. We have derived the standard error of prediction value of 0.367, standard error of estimate 0.141, and a reliable external predictivity, with a predictive r 2 (r 2 pred of 0.673. While the CoMSIA model yielded an r 2 loo of 0.719, r 2 ncv of 0.964,F value of 135.666, r 2 bs of 0.975, standard error of prediction of 0.512, standard error of estimate of 0.180, and an external predictivity with an r 2 pred of 0.611. These validation tests not only revealed the robustness of the models but also demonstrated that for our models r 2 pred, based on the mean activity of test set compounds can accurately estimate external predictivity. Conclusion: The QSAR model gave satisfactory statistical results in terms of q 2 and r 2

  9. Combined molecular docking and QSAR study of fused heterocyclic herbicide inhibitors of D1 protein in photosystem II of plants.

    Science.gov (United States)

    Funar-Timofei, Simona; Borota, Ana; Crisan, Luminita

    2017-05-01

    Cinnoline, pyridine, pyrimidine, and triazine herbicides were found be inhibitors of the D1 protein in photosystem II (D1 PSII) electron transport of plants. The photosystem II inhibitory activity of these herbicides, expressed by experimental [Formula: see text] values, was modeled by a docking and quantitative structure-activity relationships study. A conformer ensemble for each of the herbicide structure was generated using the MMFF94s force field. These conformers were further employed in a docking approach, which provided new information about the rational "active conformations" and various interaction patterns of the herbicide derivatives with D1 PSII. The most "active conformers" from the docking study were used to calculate structural descriptors, which were further related to the inhibitory experimental [Formula: see text] values by multiple linear regression (MLR). The dataset was divided into training and test sets according to the partition around medoids approach, taking 27% of the compounds from the entire series for the test set. Variable selection was performed using the genetic algorithm, and several criteria were checked for model performance. WHIM and GETAWAY geometrical descriptors (position of substituents and moieties in the molecular space) were found to contribute to the herbicidal activity. The derived MLR model is statistically significant, shows very good stability and was used to predict the herbicidal activity of new derivatives having cinnoline, indeno[1.2-c]cinnoline-ll-one, triazolo[1,5-a] pyridine, imidazo[1,2-a]pyridine, triazine and triazolo[1,5-a] pyrimidine scaffolds whose experimental inhibitory activity against D1 PSII had not been determined up to now.

  10. The use of Hasse diagrams as a potential approach for inverse QSAR.

    Science.gov (United States)

    Brüggemann, R; Pudenz, S; Carlsen, L; Sørensen, P B; Thomsen, M; Mishra, R K

    2001-02-01

    Quantitative structure-activity relationships are often based on standard multidimensional statistical analyses and sophisticated local and global molecular descriptors. Here, the aim is to develop a tool helpful to define a molecule or a class of molecules which fulfills pre-described properties, i.e., an Inverse QSAR approach. If highly sophisticated descriptors are used in QSAR, the structure and then the synthesis recipe may be hard to derive. Thus, descriptors, from which the synthesis recipe can be easily derived, seem appropriate to be included within this study. However, if descriptors simple enough to be useful for defining syntheses recipes of chemicals were used, the accuracy of a numeric expression may fail. This paper suggests a method, based on very simple elements of the theory of partially ordered sets, to find a qualitative basis for the relationship between such fairly simple descriptors on the one side and a series of ecotoxicological properties, on the other side. The partial order ranking method assumes neither linearity nor certain statistical distribution properties. Therefore the method may be more general compared to many standard statistical techniques. A series of chlorinated aliphatic compounds has been used as an illustrative example and a comparison with more sophisticated descriptors derived from quantum chemistry and graph theory is given. Among the results, it was disclosed that only for algae lethal concentration, as one of the four ecotoxicological properties, the synthesis specific predictors seem to be good estimators. For all other ecotoxicological properties quantum chemical descriptors appear as the more suitable estimators.

  11. A primer on QSAR/QSPR modeling fundamental concepts

    CERN Document Server

    Roy, Kunal; Das, Rudra Narayan

    2015-01-01

    This brief goes back to basics and describes the Quantitative structure-activity/property relationships (QSARs/QSPRs) that represent predictive models derived from the application of statistical tools correlating biological activity (including therapeutic and toxic) and properties of chemicals (drugs/toxicants/environmental pollutants) with descriptors representative of molecular structure and/or properties. It explains how the sub-discipline of Cheminformatics is used for many applications such as risk assessment, toxicity prediction, property prediction and regulatory decisions apart from drug discovery and lead optimization. The authors also present, in basic terms, how QSARs and related chemometric tools are extensively involved in medicinal chemistry, environmental chemistry and agricultural chemistry for ranking of potential compounds and prioritizing experiments. At present, there is no standard or introductory publication available that introduces this important topic to students of chemistry and phar...

  12. The antibacterial activity of some sulfonamides and sulfonyl hydrazones, and 2D-QSAR study of a series of sulfonyl hydrazones

    Science.gov (United States)

    Aslan, H. Güzin; Özcan, Servet; Karacan, Nurcan

    2012-12-01

    Benzenesulfonicacid-1-methylhydrazide (1) and its four aromatic sulfonyl hydrazone derivatives (1a-1d), N-(3-amino-2-hydroxypropyl)benzene sulfonamide (2) and N-(2-hydroxyethyl)benzenesulfonamide (3) were synthesized and their structures were determined by IR, 1H NMR, 13C NMR, and LCMS techniques. Antibacterial activities of new synthesized compounds were evaluated against various bacteria strains by microdilution and disk diffusion methods. The experimental results show that presence of OH group on sulfonamides reduces the antimicrobial activity, and antimicrobial activities of the sulfonyl hydrazones (1a-1d) are smaller than that of the parent sulfonamide (1), except Candida albicans. In addition, 2D-QSAR analysis was performed on 28 aromatic sulfonyl hydrazones as antimicrobial agents against Escherichia coli and Staphylococcus aureus. In the QSAR models, the most important descriptor is total point-charge component of the molecular dipole for E. coli, and partial negative surface area (PNSA-1) for S. aureus.

  13. Literature Review of (Q)SAR Modelling of Nanomaterial Toxicity.

    Science.gov (United States)

    Oksel, Ceyda; Ma, Cai Y; Liu, Jing J; Wilkins, Terry; Wang, Xue Z

    2017-01-01

    Despite the clear benefits that nanotechnology can bring to various sectors of industry, there are serious concerns about the potential health risks associated with engineered nanomaterials (ENMs), intensified by the limited understanding of what makes ENMs toxic and how to make them safe. As the use of ENMs for commercial purposes and the number of workers/end-users being exposed to these materials on a daily basis increases, the need for assessing the potential adverse effects of multifarious ENMs in a time- and cost-effective manner becomes more apparent. One strategy to alleviate the problem of testing a large number and variety of ENMs in terms of their toxicological properties is through the development of computational models that decode the relationships between the physicochemical features of ENMs and their toxicity. Such data-driven models can be used for hazard screening, early identification of potentially harmful ENMs and the toxicity-governing physicochemical properties, and accelerating the decision-making process by maximising the use of existing data. Moreover, these models can also support industrial, regulatory and public needs for designing inherently safer ENMs. This chapter is mainly concerned with the investigation of the applicability of (quantitative) structure-activity relationship ((Q)SAR) methods to modelling of ENMs' toxicity. It summarizes the key components required for successful application of data-driven toxicity prediction techniques to ENMs, the published studies in this field and the current limitations of this approach.

  14. A three-tier QSAR modeling strategy for estimating eye irritation potential of diverse chemicals in rabbit for regulatory purposes.

    Science.gov (United States)

    Basant, Nikita; Gupta, Shikha; Singh, Kunwar P

    2016-06-01

    Experimental determination of the eye irritation potential (EIP) of chemicals is not only tedious, time and resource intensive, it involves cruelty to test animals. In this study, we have established a three-tier QSAR modeling strategy for estimating the EIP of chemicals for the use of pharmaceutical industry and regulatory agencies. Accordingly, a qualitative (binary classification: irritating, non-irritating), semi-quantitative (four-category classification), and quantitative (regression) QSAR models employing the SDT, DTF, and DTB methods were developed for predicting the EIP of chemicals in accordance with the OECD guidelines. Structural features of chemicals responsible for eye irritation were extracted and used in QSAR analysis. The external predictive power of the developed QSAR models were evaluated through the internal and external validation procedures recommended in QSAR literature. In test data, the two and four category classification QSAR models (DTF, DTB) rendered accuracy of >93%, while the regression QSAR models (DTF, DTB) yielded correlation (R(2)) of >0.92 between the measured and predicted EIPs. Values of various statistical validation coefficients derived for the test data were above their respective threshold limits (except rm(2) in DTF), thus put a high confidence in this analysis. The applicability domain of the constructed QSAR models were defined using the descriptors range and leverage approaches. The QSAR models in this study performed better than any of the previous studies. The results suggest that the developed QSAR models can reliably predict the EIP of diverse chemicals and can be useful tools for screening of candidate molecules in the drug development process. Copyright © 2016 Elsevier Inc. All rights reserved.

  15. Development and validation of hydrophobic molecular fields derived from the quantum mechanical IEF/PCM-MST solvation models in 3D-QSAR.

    Science.gov (United States)

    Ginex, Tiziana; Muñoz-Muriedas, Jordi; Herrero, Enric; Gibert, Enric; Cozzini, Pietro; Luque, F J

    2016-05-15

    Since the development of structure-activity relationships about 50 years ago, 3D-QSAR methods belong to the most refined ligand-based in silico techniques for prediction of biological data using physicochemical molecular fields. In this scenario, this study reports the development and validation of quantum mechanical (QM)-based hydrophobic descriptors derived from the parametrized MST continuum solvation model to be used in 3D-QSAR studies within the framework of the Hydrophobic Pharmacophore (HyPhar) method. To this end, five sets of compounds reported in the literature (dopamine D2/D4 antagonists, antifungal 2-aryl-4-chromanones, and inhibitors of GSK-3, cruzain and thermolysin) have been revisited. The results derived from the QM/MST-based hydrophobic descriptors have been compared with previous CoMFA and CoMSIA studies, and examined in light of the available X-ray crystallographic structures of the targets. The analysis reveals that the combination of electrostatic and nonelectrostatic components of the octanol/water partition coefficient yields pharmacophoric models fully comparable with the predictive potential of standard 3D-QSAR techniques. Moreover, the graphical representation of the hydrophobic maps provides a direct linkage with the pattern of interactions found in crystallographic structures. Overall, the introduction of the QM/MST-based descriptors, which could be easily adapted to other continuum solvation formalisms, paves the way to novel computational strategies for disclosing structure-activity relationships in drug design. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  16. Reply to the comment of S. Rayne on "QSAR model reproducibility and applicability: A case study of rate constants of hydroxyl radical reaction models applied to polybrominated diphenyl ethers and (benzo-)triazoles".

    Science.gov (United States)

    Gramatica, Paola; Kovarich, Simona; Roy, Partha Pratim

    2013-07-30

    We appreciate the interest of Dr. Rayne on our article and we completely agree that the dataset of (benzo-)triazoles, which were screened by the hydroxyl radical reaction quantitative structure-activity relationship (QSAR) model, was not only composed of benzo-triazoles but also included some simpler triazoles (without the condensed benzene ring), such as the chemicals listed by Dr. Rayne, as well as some related heterocycles (also few not aromatic). We want to clarify that in this article (as well as in other articles in which the same dataset was screened), for conciseness, the abbreviations (B)TAZs and BTAZs were used as general (and certainly too simplified) notations meaning an extended dataset of benzo-triazoles, triazoles, and related compounds. Copyright © 2013 Wiley Periodicals, Inc.

  17. The QSAR and docking calculations of fullerene derivatives as HIV-1 protease inhibitors

    Science.gov (United States)

    Saleh, Noha A.

    2015-02-01

    The inhibition of HIV-1 protease is considered as one of the most important targets for drug design and the deactivation of HIV-1. In the present work, the fullerene surface (C60) is modified by adding oxygen atoms as well as hydroxymethylcarbonyl (HMC) groups to form 6 investigated fullerene derivative compounds. These compounds have one, two, three, four or five O atoms + HMC groups at different positions on phenyl ring. The effect of the repeating of these groups on the ability of suggested compounds to inhibit the HIV protease is studied by calculating both Quantitative Structure Activity Relationship (QSAR) properties and docking simulation. Based on the QSAR descriptors, the solubility and the hydrophilicity of studied fullerene derivatives increased with increasing the number of oxygen atoms + HMC groups in the compound. While docking calculations indicate that, the compound with two oxygen atoms + HMC groups could interact and binds with HIV-1 protease active site. This is could be attributed to the active site residues of HIV-1 protease are hydrophobic except the two aspartic acids. So that, the increase in the hydrophilicity and polarity of the compound is preventing and/or decreasing the hydrophobic interaction between the compound and HIV-1 protease active site.

  18. Antibacterial activity and QSAR of chalcones against biofilm-producing bacteria isolated from marine waters.

    Science.gov (United States)

    Sivakumar, P M; Prabhawathi, V; Doble, M

    2010-04-01

    Biofouling in the marine environment is a major problem. In this study, three marine organisms, namely Bacillus flexus (LD1), Pseudomonas fluorescens (MD3) and Vibrio natriegens (MD6), were isolated from biofilms formed on polymer and metal surfaces immersed in ocean water. Phylogenetic analysis of these three organisms indicated that they were good model systems for studying marine biofouling. The in vitro antifouling activity of 47 synthesized chalcone derivatives was investigated by estimating the minimum inhibitory concentration against these organisms using a twofold dilution technique. Compounds C-5, C-16, C-24, C-33, C-34 and C-37 were found to be the most active. In the majority of the cases it was found that these active compounds had hydroxyl substitutions. A quantitative structure-activity relationship (QSAR) was developed after dividing the total data into training and test sets. The statistical measures r(2), [image omitted] (>0.6) q(2) (>0.5) and the F-ratio were found to be satisfactory. Spatial, structural and electronic descriptors were found to be predominantly affecting the antibiofouling activity of these compounds. Among the spatial descriptors, Jurs descriptors showed their contribution in all the three antibacterial QSARs.

  19. Synthesis, cytotoxicity, and QSAR study of new aza-cyclopenta[b]fluorene-1,9-dione derivatives.

    Science.gov (United States)

    Miri, Ramin; Firuzi, Omidreza; Peymani, Payam; Zamani, Meysam; Mehdipour, Ahmad Reza; Heydari, Zahra; Farahani, Maryam Masteri; Shafiee, Abbas

    2012-01-01

    Thirty novel derivatives of aza-cyclopenta[b]fluorene-1,9-dione were synthesized, and their cytotoxic activities were tested against HeLa, LS180, MCF-7, and Raji cancer cell lines by MTT assay. Two derivatives containing nitrofuryl moiety, including 10-(5-nitro-furan-2-yl)-2,3-dihydro-4-aza-cyclopenta[b]fluorene-1,9-dione (IC(50) range: 5.7-13.0 μm) and 10-(5-Nitro-furan-2-yl)-2,3,4,10-tetrahydro-4-aza-cyclopenta[b]fluorene-1,9-dione (IC(50) range: 3.6-20.2 μm), as well as 10-(2-Nitro-phenyl)-2,3,4,10-tetrahydro-4-aza-cyclopenta[b]fluorene-1,9-dione (IC(50) range: 3.1-27.1 μm) with nitrophenyl moiety on C10 position, were the most effective compounds. Furthermore, the effect of physiochemical descriptors on the cytotoxicity was evaluated by quantitative structure-activity relationship analysis. The quantitative structure-activity relationship results showed that molecular dipole moment, molar refractivity, fragment-based parameters, and some topological indices were influential on the cytotoxic effect. Finally, the good correlation that was found among cytotoxic data obtained from different cell lines may be an implication of a common cytotoxic mechanism in these cell lines. These findings provide useful structural information for the rational design and synthesis of efficient chemotherapeutic agents for treatment for cancer. © 2011 John Wiley & Sons A/S.

  20. AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment

    Directory of Open Access Journals (Sweden)

    Stålring Jonna C

    2011-07-01

    Full Text Available Abstract Background Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. Results This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require programming knowledge as flexible applications can be created, not only at a scripting level, but also in a graphical programming environment. Conclusions AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the

  1. AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment.

    Science.gov (United States)

    Stålring, Jonna C; Carlsson, Lars A; Almeida, Pedro; Boyer, Scott

    2011-07-28

    Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require programming knowledge as flexible applications can be created, not only at a scripting level, but also in a graphical programming environment. AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of highly accurate QSAR models

  2. QSAR for cholinesterase inhibition by organophosphorus esters and CNDO/2 calculations for organophosphorus ester hydrolysis. [quantitative structure-activity relationship, complete neglect of differential overlap

    Science.gov (United States)

    Johnson, H.; Kenley, R. A.; Rynard, C.; Golub, M. A.

    1985-01-01

    Quantitative structure-activity relationships were derived for acetyl- and butyrylcholinesterase inhibition by various organophosphorus esters. Bimolecular inhibition rate constants correlate well with hydrophobic substituent constants, and with the presence or absence of cationic groups on the inhibitor, but not with steric substituent constants. CNDO/2 calculations were performed on a separate set of organophosphorus esters, RR-primeP(O)X, where R and R-prime are alkyl and/or alkoxy groups and X is fluorine, chlorine or a phenoxy group. For each subset with the same X, the CNDO-derived net atomic charge at the central phosphorus atom in the ester correlates well with the alkaline hydrolysis rate constant. For the whole set of esters with different X, two equations were derived that relate either charge and leaving group steric bulk, or orbital energy and bond order to the hydrolysis rate constant.

  3. Evaluation of joint toxicity of nitroaromatic compounds and copper to Photobacterium phosphoreum and QSAR analysis.

    Science.gov (United States)

    Su, Limin; Zhang, Xujia; Yuan, Xing; Zhao, Yuanhui; Zhang, Dongmei; Qin, Weichao

    2012-11-30

    The individual toxicities of Cu and 11 nitroaromatic compounds to Photobacterium phosphoreum were determined. The toxicity was expressed as the concentrations causing a 50% inhibition of bioluminescence after 15 min exposure (IC(50)). To evaluate the joint effect between the metal ion and the 11 nitroaromatic compounds, the joint toxicity of Cu and 11 nitroaromatic compounds were measured at different Cu concentrations (0.2IC(50), 0.5IC(50) and 0.8IC(50)), respectively. The result shows that the binary joint effect between Cu and nitroaromatic compounds is mainly simple addition at the low Cu concentration (0.2IC(50)). However, an antagonism effect, 55% and 64%, was observed between Cu and 11 nitroaromatic compounds for Cu at medium and high concentrations (0.5IC(50) and 0.8IC(50)). Quantitative structure-activity relationship (QSAR) analysis was performed to study the joint toxicity for the 11 nitroaromatic compounds. The result shows that the toxicity of nitroaromatic compounds is related to descriptors of Connolly solvent-excluded volume (CSEV) and dipolarity/polarizability (S) at low Cu concentration. On the other hand, the toxicity is related to Connolly accessible area (CAA) at medium and high Cu concentrations. The result indicates that different QSAR models on complex mixtures need to be developed to assess the ecological risk in real environments. Using single toxic data to evaluate the toxic effect of mixtures may result in wrong conclusions. Copyright © 2012 Elsevier B.V. All rights reserved.

  4. Combinatorial Pharmacophore-Based 3D-QSAR Analysis and Virtual Screening of FGFR1 Inhibitors

    Directory of Open Access Journals (Sweden)

    Nannan Zhou

    2015-06-01

    Full Text Available The fibroblast growth factor/fibroblast growth factor receptor (FGF/FGFR signaling pathway plays crucial roles in cell proliferation, angiogenesis, migration, and survival. Aberration in FGFRs correlates with several malignancies and disorders. FGFRs have proved to be attractive targets for therapeutic intervention in cancer, and it is of high interest to find FGFR inhibitors with novel scaffolds. In this study, a combinatorial three-dimensional quantitative structure-activity relationship (3D-QSAR model was developed based on previously reported FGFR1 inhibitors with diverse structural skeletons. This model was evaluated for its prediction performance on a diverse test set containing 232 FGFR inhibitors, and it yielded a SD value of 0.75 pIC50 units from measured inhibition affinities and a Pearson’s correlation coefficient R2 of 0.53. This result suggests that the combinatorial 3D-QSAR model could be used to search for new FGFR1 hit structures and predict their potential activity. To further evaluate the performance of the model, a decoy set validation was used to measure the efficiency of the model by calculating EF (enrichment factor. Based on the combinatorial pharmacophore model, a virtual screening against SPECS database was performed. Nineteen novel active compounds were successfully identified, which provide new chemical starting points for further structural optimization of FGFR1 inhibitors.

  5. Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 2. Application of the novel 3D molecular descriptors to QSAR/QSPR studies.

    Science.gov (United States)

    Consonni, Viviana; Todeschini, Roberto; Pavan, Manuela; Gramatica, Paola

    2002-01-01

    In a previous paper the theory of the new molecular descriptors called GETAWAY (GEometry, Topology, and Atom-Weights AssemblY) was explained. These descriptors have been proposed with the aim of matching 3D-molecular geometry, atom relatedness, and chemical information. In this paper prediction ability in structure-property correlations of GETAWAY descriptors has been tested extensively by analyzing the regressions of these descriptors for selected properties of some reference compound classes. Moreover, the general performance of the new descriptors in QSAR/QSPR has been evaluated with respect to other well-known sets of molecular descriptors.

  6. 3D-QSAR, molecular dynamics simulations, and molecular docking studies on pyridoaminotropanes and tetrahydroquinazoline as mTOR inhibitors.

    Science.gov (United States)

    Chaube, Udit; Bhatt, Hardik

    2017-06-02

    Cancer is a second major disease after metabolic disorders where the number of cases of death is increasing gradually. Mammalian target of rapamycin (mTOR) is one of the most important targets for treatment of cancer, specifically for breast and lung cancer. In the present research work, Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) studies were performed on 50 compounds reported as mTOR inhibitors. Three different alignment methods were used, and among them, distill method was found to be the best method. In CoMFA, leave-one-out cross-validated coefficients [Formula: see text], conventional coefficient [Formula: see text], and predicted correlation coefficient [Formula: see text] values were found to be 0.664, 0.992, and 0.652, respectively. CoMSIA study was performed in 25 different combinations of features, such as steric, electrostatic, hydrogen bond donor, hydrogen bond acceptor, and hydrophobic. From this, a combination of steric, electrostatic, hydrophobic (SEH), and a combination of steric, electrostatic, hydrophobic, donor, and acceptor (SEHDA) were found as best combinations. In CoMSIA (SEHDA), [Formula: see text], [Formula: see text] and [Formula: see text] were found to be 0.646, 0.977, and 0.682, respectively, while in the case of CoMSIA (SEH), the values were 0.739, 0.976, and 0.779, respectively. Contour maps were generated and validated by molecular dynamics simulation-assisted molecular docking study. Highest active compound 19, moderate active compound 15, and lowest active compound 42 were docked on mTOR protein to validate the results of our molecular docking study. The result of the molecular docking study of highest active compound 19 is in line with the outcomes generated by contour maps. Based on the features obtained through this study, six novel mTOR inhibitors were designed and docked. This study could be useful for designing novel molecules with increased anticancer activity.

  7. Sigma-2 receptor ligands QSAR model dataset

    Directory of Open Access Journals (Sweden)

    Antonio Rescifina

    2017-08-01

    Full Text Available The data have been obtained from the Sigma-2 Receptor Selective Ligands Database (S2RSLDB and refined according to the QSAR requirements. These data provide information about a set of 548 Sigma-2 (σ2 receptor ligands selective over Sigma-1 (σ1 receptor. The development of the QSAR model has been undertaken with the use of CORAL software using SMILES, molecular graphs and hybrid descriptors (SMILES and graph together. Data here reported include the regression for σ2 receptor pKi QSAR models. The QSAR model was also employed to predict the σ2 receptor pKi values of the FDA approved drugs that are herewith included.

  8. Docking Based 3D-QSAR Study of Tricyclic Guanidine Analogues of Batzelladine K As Anti-Malarial Agents

    OpenAIRE

    Ahmed, Nafees; Anwar, Sirajudheen; Thet Htar, Thet

    2017-01-01

    The Plasmodium falciparum Lactate Dehydrogenase enzyme (PfLDH) catalyzes inter-conversion of pyruvate to lactate during glycolysis producing the energy required for parasitic growth. The PfLDH has been studied as a potential molecular target for development of anti-malarial agents. In an attempt to find the potent inhibitor of PfLDH, we have used Discovery studio to perform molecular docking in the active binding pocket of PfLDH by CDOCKER, followed by three-dimensional quantitative structure...

  9. Development of quantitative structure-activity relationship (QSAR) models to predict the carcinogenic potency of chemicals I. Alternative toxicity measures as an estimator of carcinogenic potency.

    Science.gov (United States)

    Venkatapathy, Raghuraman; Wang, Ching Yi; Bruce, Robert Mark; Moudgal, Chandrika

    2009-01-15

    Determining the carcinogenicity and carcinogenic potency of new chemicals is both a labor-intensive and time-consuming process. In order to expedite the screening process, there is a need to identify alternative toxicity measures that may be used as surrogates for carcinogenic potency. Alternative toxicity measures for carcinogenic potency currently being used in the literature include lethal dose (dose that kills 50% of a study population [LD(50)]), lowest-observed-adverse-effect-level (LOAEL) and maximum tolerated dose (MTD). The purpose of this study was to investigate the correlation between tumor dose (TD(50)) and three alternative toxicity measures as an estimator of carcinogenic potency. A second aim of this study was to develop a Classification and Regression Tree (CART) between TD(50) and estimated/experimental predictor variables to predict the carcinogenic potency of new chemicals. Rat TD(50)s of 590 structurally diverse chemicals were obtained from the Cancer Potency Database, and the three alternative toxicity measures considered in this study were estimated using TOPKAT, a toxicity estimation software. Though poor correlations were obtained between carcinogenic potency and the three alternative toxicity (both experimental and TOPKAT) measures for the CPDB chemicals, a CART developed using experimental data with no missing values as predictor variables provided reasonable estimates of TD(50) for nine chemicals that were part of an external validation set. However, if experimental values for the three alternative measures, mutagenicity and logP are not available in the literature, then either the CART developed using missing experimental values or estimated values may be used for making a prediction.

  10. QSAR models for oxidation of organic micropollutants in water based on ozone and hydroxyl radical rate constants and their chemical classification

    KAUST Repository

    Sudhakaran, Sairam

    2013-03-01

    Ozonation is an oxidation process for the removal of organic micropollutants (OMPs) from water and the chemical reaction is governed by second-order kinetics. An advanced oxidation process (AOP), wherein the hydroxyl radicals (OH radicals) are generated, is more effective in removing a wider range of OMPs from water than direct ozonation. Second-order rate constants (kOH and kO3) are good indices to estimate the oxidation efficiency, where higher rate constants indicate more rapid oxidation. In this study, quantitative structure activity relationships (QSAR) models for O3 and AOP processes were developed, and rate constants, kOH and kO3, were predicted based on target compound properties. The kO3 and kOH values ranged from 5 * 10-4 to 105 M-1s-1 and 0.04 to 18 * (109) M-1 s-1, respectively. Several molecular descriptors which potentially influence O3 and OH radical oxidation were identified and studied. The QSAR-defining descriptors were double bond equivalence (DBE), ionisation potential (IP), electron-affinity (EA) and weakly-polar component of solvent accessible surface area (WPSA), and the chemical and statistical significance of these descriptors was discussed. Multiple linear regression was used to build the QSAR models, resulting in high goodness-of-fit, r2 (>0.75). The models were validated by internal and external validation along with residual plots. © 2012 Elsevier Ltd.

  11. Analysis of the internal representations developed by neural networks for structures applied to quantitative structure--activity relationship studies of benzodiazepines.

    Science.gov (United States)

    Micheli, A; Sperduti, A; Starita, A; Bianucci, A M

    2001-01-01

    An application of recursive cascade correlation (CC) neural networks to quantitative structure-activity relationship (QSAR) studies is presented, with emphasis on the study of the internal representations developed by the neural networks. Recursive CC is a neural network model recently proposed for the processing of structured data. It allows the direct handling of chemical compounds as labeled ordered directed graphs, and constitutes a novel approach to QSAR. The adopted representation of molecular structure captures, in a quite general and flexible way, significant topological aspects and chemical functionalities for each specific class of molecules showing a particular chemical reactivity or biological activity. A class of 1,4-benzodiazepin-2-ones is analyzed by the proposed approach. It compares favorably versus the traditional QSAR treatment based on equations. To show the ability of the model in capturing most of the structural features that account for the biological activity, the internal representations developed by the networks are analyzed by principal component analysis. This analysis shows that the networks are able to discover relevant structural features just on the basis of the association between the molecular morphology and the target property (affinity).

  12. In vivo toxicity of nitroaromatics: A comprehensive quantitative structure-activity relationship study.

    Science.gov (United States)

    Gooch, Aminah; Sizochenko, Natalia; Rasulev, Bakhtiyor; Gorb, Leonid; Leszczynski, Jerzy

    2017-08-01

    The toxicity data of 90 nitroaromatic compounds related to their 50% lethal dose concentration for rats (LD50) were analyzed to develop quantitative structure-activity relationship (QSAR) models. Quantum-chemically calculated descriptors together with molecular descriptors generated by DRAGON, PaDEL, and HiT-QSAR software were utilized to build QSAR models. Quality and validity of the models were determined by internal and external validation techniques. The results show that the toxicity of nitroaromatic compounds depends on various factors, such as the number of nitro-groups, the topological state, and the presence of certain structural fragments. The developed models based on the largest (to date) dataset of nitroaromatics in vivo toxicity showed a good predictive ability. The results provide important input that could be applied in a preliminary assessment of nitroaromatic compounds' toxicity to mammals. Environ Toxicol Chem 2017;36:2227-2233. © 2017 SETAC. © 2017 SETAC.

  13. 3D QSAR studies on binding affinities of coumarin natural products for glycosomal GAPDH of Trypanosoma cruzi

    Science.gov (United States)

    Menezes, Irwin R. A.; Lopes, Julio C. D.; Montanari, Carlos A.; Oliva, Glaucius; Pavão, Fernando; Castilho, Marcelo S.; Vieira, Paulo C.; Pupo, M.^onica T.

    2003-05-01

    Drug design strategies based on Comparative Molecular Field Analysis (CoMFA) have been used to predict the activity of new compounds. The major advantage of this approach is that it permits the analysis of a large number of quantitative descriptors and uses chemometric methods such as partial least squares (PLS) to correlate changes in bioactivity with changes in chemical structure. Because it is often difficult to rationalize all variables affecting the binding affinity of compounds using CoMFA solely, the program GRID was used to describe ligands in terms of their molecular interaction fields, MIFs. The program VolSurf that is able to compress the relevant information present in 3D maps into a few descriptors can treat these GRID fields. The binding affinities of a new set of compounds consisting of 13 coumarins, for one of which the three-dimensional ligand-enzyme bound structure is known, were studied. A final model based on the mentioned programs was independently validated by synthesizing and testing new coumarin derivatives. By relying on our knowledge of the real physical data (i.e., combining crystallographic and binding affinity results), it is also shown that ligand-based design agrees with structure-based design. The compound with the highest binding affinity was the coumarin chalepin, isolated from Rutaceae species, with an IC50 value of 55.5 μM towards the enzyme glyceraldehyde-3-phosphate dehydrogenase (gGAPDH) from glycosomes of the parasite Trypanosoma cruzi, the causative agent of Chagas' disease. The proposed models from GRID MIFs have revealed the importance of lipophilic interactions in modulating the inhibition, but without excluding the dependence on stereo-electronic properties as found from CoMFA fields.

  14. QSAR models for predicting in vivo aquatic toxicity of chlorinated alkanes to fish

    NARCIS (Netherlands)

    Zvinavashe, E.; Berg, H. van den; Soffers, A.E.M.F.; Vervoort, J.; Freidig, A.; Murk, A.J.; Rietjens, I.M.C.M.

    2008-01-01

    Quantitative structure-activity relationship (QSAR) models are expected to play a crucial role in reducing the number of animals to be used for toxicity testing resulting from the adoption of the new European Union chemical control system called Registration, Evaluation, and Authorization of

  15. Elaborate ligand-based modeling coupled with multiple linear regression and k nearest neighbor QSAR analyses unveiled new nanomolar mTOR inhibitors.

    Science.gov (United States)

    Khanfar, Mohammad A; Taha, Mutasem O

    2013-10-28

    The mammalian target of rapamycin (mTOR) has an important role in cell growth, proliferation, and survival. mTOR is frequently hyperactivated in cancer, and therefore, it is a clinically validated target for cancer therapy. In this study, we combined exhaustive pharmacophore modeling and quantitative structure-activity relationship (QSAR) analysis to explore the structural requirements for potent mTOR inhibitors employing 210 known mTOR ligands. Genetic function algorithm (GFA) coupled with k nearest neighbor (kNN) and multiple linear regression (MLR) analyses were employed to build self-consistent and predictive QSAR models based on optimal combinations of pharmacophores and physicochemical descriptors. Successful pharmacophores were complemented with exclusion spheres to optimize their receiver operating characteristic curve (ROC) profiles. Optimal QSAR models and their associated pharmacophore hypotheses were validated by identification and experimental evaluation of several new promising mTOR inhibitory leads retrieved from the National Cancer Institute (NCI) structural database. The most potent hit illustrated an IC50 value of 48 nM.

  16. Design of Novel Chemotherapeutic Agents Targeting Checkpoint Kinase 1 Using 3D-QSAR Modeling and Molecular Docking Methods.

    Science.gov (United States)

    Balupuri, Anand; Balasubramanian, Pavithra K; Cho, Seung J

    2016-01-01

    Checkpoint kinase 1 (Chk1) has emerged as a potential therapeutic target for design and development of novel anticancer drugs. Herein, we have performed three-dimensional quantitative structure-activity relationship (3D-QSAR) and molecular docking analyses on a series of diazacarbazoles to design potent Chk1 inhibitors. 3D-QSAR models were developed using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. Docking studies were performed using AutoDock. The best CoMFA and CoMSIA models exhibited cross-validated correlation coefficient (q2) values of 0.631 and 0.585, and non-cross-validated correlation coefficient (r2) values of 0.933 and 0.900, respectively. CoMFA and CoMSIA models showed reasonable external predictabilities (r2 pred) of 0.672 and 0.513, respectively. A satisfactory performance in the various internal and external validation techniques indicated the reliability and robustness of the best model. Docking studies were performed to explore the binding mode of inhibitors inside the active site of Chk1. Molecular docking revealed that hydrogen bond interactions with Lys38, Glu85 and Cys87 are essential for Chk1 inhibitory activity. The binding interaction patterns observed during docking studies were complementary to 3D-QSAR results. Information obtained from the contour map analysis was utilized to design novel potent Chk1 inhibitors. Their activities and binding affinities were predicted using the derived model and docking studies. Designed inhibitors were proposed as potential candidates for experimental synthesis.

  17. Computational evaluation of some indenopyrazole derivatives as anticancer compounds; application of QSAR and docking methodologies.

    Science.gov (United States)

    Shahlaei, Mohsen; Fassihi, Afshin; Saghaie, Lotfollah; Arkan, Elham; Madadkar-Sobhani, Armin; Pourhossein, Alireza

    2013-02-01

    A computational procedure was performed on some indenopyrazole derivatives. Two important procedures in computational drug discovery, namely docking for modeling ligand-receptor interactions and quantitative structure activity relationships were employed. MIA-QSAR analysis of the studied derivatives produced a model with high predictability. The developed model was then used to evaluate the bioactivity of 54 proposed indenopyrazole derivatives. In order to confirm the obtained results through this ligand-based method, docking was performed on the selected compounds. An ADME-Tox evaluation was also carried out to search for more suitable compounds. Satisfactory bioactivities and ADME-Tox profiles for two of the compounds, namely 62 and S13, propose that further studies should be performed on such devoted chemical structures.

  18. Evaluation of the pharmacological descriptors related to the induction of antidepressant activity and its prediction by QSAR/QRAR methods.

    Science.gov (United States)

    Avram, S; Buiu, C; Duda-Seiman, D; Duda-Seiman, C; Borcan, F; Mihailescu, D

    2012-06-01

    Antidepressants are psychiatric agents used for the treatment of different types of depression, being at present amongst the most commonly prescribed drugs, while their effectiveness and adverse effects are still the subject of many studies. To reduce the inefficiency of known antidepressants caused by their side-effects, many research efforts have recently focused on the development of improved strategies for new antidepressants drug design. For this reason it is necessary to apply very fast and precise techniques, such as QSAR (Quantitative Structure-Activity Relationships) and QRAR (Quantitative Retention-Activity Relationship), which are capable to analyze and predict the biological activity for these structures, taking in account the possible changes of the molecular structures and chromatographic parameters. We discuss the pharmaceutical descriptors (van der Waals, electrostatic, hydrophobicity, hydrogen donor/acceptor bond, Verloop's parameters, polar area) involved in QSAR and also chromatographic parameters involved in QRAR studies of antidepressants. Antidepressant activities of alkanol piperazine, acetamides, arylpiperazines, thienopyrimidinone derivatives (as preclinical antidepressants) and also the antidepressants already used in clinical practice are mentioned.

  19. Combined 3D-QSAR, molecular docking, molecular dynamics simulation, and binding free energy calculation studies on the 5-hydroxy-2H-pyridazin-3-one derivatives as HCV NS5B polymerase inhibitors.

    Science.gov (United States)

    Yu, Haijing; Fang, Yu; Lu, Xia; Liu, Yongjuan; Zhang, Huabei

    2014-01-01

    The NS5B RNA-dependent RNA polymerase (RdRP) is a promising therapeutic target for developing novel anti-hepatitis C virus (HCV) drugs. In this work, a combined molecular modeling study was performed on a series of 193 5-hydroxy-2H-pyridazin-3-one derivatives as inhibitors of HCV NS5B Polymerase. The best 3D-QSAR models, including CoMFA and CoMSIA, are based on receptor (or docking). Furthermore, a 40-ns molecular dynamics (MD) simulation and binding free energy calculations using docked structures of NS5B with ten compounds, which have diverse structures and pIC50 values, were employed to determine the detailed binding process and to compare the binding modes of the inhibitors with different activities. On one side, the stability and rationality of molecular docking and 3D-QSAR results were validated by MD simulation. The binding free energies calculated by the MM-PBSA method gave a good correlation with the experimental biological activity. On the other side, by analyzing some differences between the molecular docking and the MD simulation results, we can find that the MD simulation could also remedy the defects of molecular docking. The analyses of the combined molecular modeling results have identified that Tyr448, Ser556, and Asp318 are the key amino acid residues in the NS5B binding pocket. The results from this study can provide some insights into the development of novel potent NS5B inhibitors. © 2013 John Wiley & Sons A/S.

  20. Employing conformational analysis in the molecular modeling of agrochemicals: insights on QSAR parameters of 2,4-D

    Directory of Open Access Journals (Sweden)

    Matheus Puggina de Freitas

    2013-12-01

    Full Text Available A common practice to compute ligand conformations of compounds with various degrees of freedom to be used in molecular modeling (QSAR and docking studies is to perform a conformational distribution based on repeated random sampling, such as Monte-Carlo methods. Further calculations are often required. This short review describes some methods used for conformational analysis and the implications of using selected conformations in QSAR. A case study is developed for 2,4-dichlorophenoxyacetic acid (2,4-D, a widely used herbicide which binds to TIR1 ubiquitin ligase enzyme. The use of such an approach and semi-empirical calculations did not achieve all possible minima for 2,4-D. In addition, the conformations and respective energies obtained by the semi-empirical AM1 method do not match the calculated trends obtained by a high level DFT method. Similar findings were obtained for the carboxylate anion, which is the bioactive form. Finally, the crystal bioactive structure of 2,4-D was not found as a minimum when using Monte-Carlo/AM1 and is similarly populated with another conformer in implicit water solution according to optimization at the B3LYP/aug-cc-pVDZ level. Therefore, quantitative structure-activity relationship (QSAR methods based on three dimensional chemical structures are not fundamental to provide predictive models for 2,4-D congeners as TIR1 ubiquitin ligase ligands, since they do not necessarily reflect the bioactive conformation of this molecule. This probably extends to other systems.

  1. 3D-QSAR Investigation of Synthetic Antioxidant Chromone Derivatives by Molecular Field Analysis

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    Jiraporn Ungwitayatorn

    2008-02-01

    Full Text Available A series of 7-hydroxy, 8-hydroxy and 7,8-dihydroxy synthetic chromone derivatives was evaluated for their DPPH free radical scavenging activities. A training set of 30 synthetic chromone derivatives was subject to three-dimensional quantitative structure-activity relationship (3D-QSAR studies using molecular field analysis (MFA. The substitutional requirements for favorable antioxidant activity were investigated and a predictive model that could be used for the design of novel antioxidants was derived. Regression analysis was carried out using genetic partial least squares (G/PLS method. A highly predictive and statistically significant model was generated. The predictive ability of the developed model was assessed using a test set of 5 compounds (r2pred = 0.924. The analyzed MFA model demonstrated a good fit, having r2 value of 0.868 and crossvalidated coefficient r2cv value of 0.771.

  2. 3D-QSAR model of flavonoids binding at benzodiazepine site in GABAA receptors.

    Science.gov (United States)

    Huang, X; Liu, T; Gu, J; Luo, X; Ji, R; Cao, Y; Xue, H; Wong, J T; Wong, B L; Pei, G; Jiang, H; Chen, K

    2001-06-07

    With flavone as a structural template, three-dimensional quantitative structure-activity relationship (3D-QSAR) studies and ab initio calculations were performed on a series of flavonoids. A reasonable pharmacophore model was built through CoMFA, CoMSIA, and HQSAR analyses and electrostatic potential calculations. A plausible binding mode for flavonoids with GABA(A) receptors was rationalized. On the basis of the commonly recognized binding site, the specific S1 and S2 subsites relating to substituent positions were proposed. The different binding affinities could be explained according to the frontier orbitals and electrostatic potential (ESP) maps. The ESP could be used as a novel starting point for designing more selective BZ-binding-site ligands.

  3. A quantitative structure–activity relationship study of tetrabutylphosphonium bromide analogs as muscarinic acetylcholine receptors agonists

    Directory of Open Access Journals (Sweden)

    MEHDI NEKOEI

    2011-08-01

    Full Text Available Quantitative structure–activity relationship (QSAR of tetrabutyl­phosphonium bromide (TBPB analogs as muscarinic acetylcholine receptors (mAChRs agonists was studied. A suitable set of molecular descriptors was calculated and stepwise multiple linear regression (SW-MLR was employed to select those descriptors that resulted in the best fitted models. A MLR model with three selected descriptors was obtained. Furthermore, the MLR model was va­lidated using the leave-one-out (LOO and leave-group-out (LGO cross-vali­dation, and the Y-randomization test. This model, with high statistical signifi­cance (R2train = 0.982, F = 388.715, Q2LOO = 0.973, Q2LGO = 0.977 and R2test = 0.986 could predict the activity of the molecules with a percentage predic­tion error lower than 5 %.

  4. A natural chalcone induces apoptosis in lung cancer cells: 3D-QSAR, docking and an in vivo/vitro assay

    OpenAIRE

    Chen, Gang; Zhou, Di; Li, Xue-Zheng; Jiang, Zhe; Tan, Chengyu; Wei, Xiu-Yan; Ling, Junhong; Jing, Jing; Liu, Fen; Li, Ning

    2017-01-01

    This study was to study the antitumor effect of lonchocarpin (34) from traditional herbal medicine Pongamia pinnata (L.) Pierre and to reveal the underlying mechanism. The cytotoxic activities of lonchocarpin were evaluated in 10 lung cancer cell lines and it exhibited 97.5% activity at a dose of 100??M in the H292 cell line. A field-based quantitative structure-activity relationship (3D-QSAR) study of 37 flavonoids from P. pinnata was also performed, and the results obtained showed that the ...

  5. A refined QSAR model for prediction of chemical asthma hazard.

    Science.gov (United States)

    Jarvis, J; Seed, M J; Stocks, S J; Agius, R M

    2015-11-01

    A previously developed quantitative structure-activity relationship (QSAR) model has been extern ally validated as a good predictor of chemical asthma hazard (sensitivity: 79-86%, specificity: 93-99%). To develop and validate a second version of this model. Learning dataset asthmagenic chemicals with molecular weight (MW) chemicals for which no reported case(s) of occupational asthma had been identified were selected at random from UK and US occupational exposure limit tables. MW banding was used in an attempt to categorically match the control group for MW distribution of the asthmagens. About 10% of chemicals in each MW category were excluded for use as an external validation set. An independent researcher utilized a logistic regression approach to compare the molecular descriptors present in asthmagens and controls. The resulting equation generated a hazard index (HI), with a value between zero and one, as an estimate of the probability that the chemical had asthmagenic potential. The HI was determined for each compound in the external validation set. The model development sets comprised 99 chemical asthmagens and 204 controls. The external validation showed that using a cut-point HI of 0.39, 9/10 asthmagenic (sensitivity: 90%) and 23/24 non-asthmagenic (specificity: 96%) compounds were correctly predicted. The new QSAR model showed a better receiver operating characteristic plot than the original. QSAR refinement by iteration has resulted in an improved model for the prediction of chemical asthma hazard. © The Author 2015. Published by Oxford University Press on behalf of the Society of Occupational Medicine. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  6. Performance of Kier-Hall E-state Descriptors in Quantitative Structure Activity Relationship (QSAR Studies of Multifunctional Molecules

    Directory of Open Access Journals (Sweden)

    Darko Butina

    2004-12-01

    Full Text Available Performance of the E-state descriptors was tested against simple counts of the 35 atom types that the Kier-Hall E-states are based upon, by building PLS models for clogP, aqueous solubility, human intestinal absorption (HIA and blood brain barrier (BBB. The results indicate that the simple counts work at least as well as E-state descriptors in building models for solubility and BBB, while surprisingly, simple counts have outperformed E-states by 18% and 30%, respectively, when building the models for HIA and clogP.

  7. Quantitative structure-activity relationships in fish toxicity studies. Part 1: relationship for 50 industrial pollutants

    Energy Technology Data Exchange (ETDEWEB)

    Koenemann, H.

    1981-01-01

    LC50-experiments have been conducted using guppies subjected to 72 industrial pollutants. The correlation of the LC50 with several expressions of the hydrophobicity of these chemicals has been studied. Calculated log Poct-values appeared to satisfy more than HPLC retention indices, solubility data or molecular connectivity indices. One QSAR, with log Poct as the only variable, gave good estimations of the toxicity of most of the tested compounds with log Poct less than 6. No LC50 could be determined for solutions of compounds with log Poct greater than 6.

  8. QSAR Methods to Screen Endocrine Disruptors

    Directory of Open Access Journals (Sweden)

    Nicola Porta

    2016-08-01

    Full Text Available The identification of endocrine disrupting chemicals (EDCs is one of the important goals of environmental chemical hazard screening. We report on in silico methods addressing toxicological studies about EDCs with a special focus on the application of QSAR models for screening purpose. Since Estrogen-like (ER activity has been extensively studied, the majority of the available models are based on ER-related endpoints. Some of these models are here reviewed and described. As example for their application, we screen an assembled dataset of candidate substitutes for some known EDCs belonging to the chemical classes of phthalates, bisphenols and parabens, selected considering their toxicological relevance and broad application, with the general aim of preliminary assessing their ED potential. The goal of the substitution processes is to advance inherently safer chemicals and products, consistent with the principles of green chemistry. Results suggest that the integration of a family of different models accounting for different endpoints can be a convenient way to describe ED as properly as possible and allow also both to increase the confidence of the predictions and to maximize the probability that most active compounds are correctly found.

  9. Synthesis and QSAR study of novel cytotoxic spiro[3H-indole-3,2'(1'H)-pyrrolo[3,4-c]pyrrole]-2,3',5'(1H,2'aH,4'H)-triones.

    Science.gov (United States)

    Girgis, Adel S; Stawinski, Jacek; Ismail, Nasser S M; Farag, Hanaa

    2012-01-01

    1,3-Dipolar cycloaddition reaction of 1-aryl-1H-pyrrole-2,5-diones 1a-e with non-stabilized azomethine ylides, generated in situ via decarboxylative condensation of isatins 2a-c and sarcosine (3) in refluxing ethanol, afforded 4'-aryl-5'a,6'-dihydro-1'-methyl-spiro[3H-indole-3,2'(1'H)-pyrrolo[3,4-c]pyrrole]-2,3',5'(1H,2'aH,4'H)-triones 4a-o in good yields. Compound 4l exhibited high anti-tumor activity against HEPG2 (liver cancer) cell line (IC(50) = 12.16 μM) compared to that of Doxorubicin (IC(50) = 7.36 μM), and the other synthesized compounds revealed moderate anti-tumor properties against HCT116 (colon), MCF7 (breast) and HEPG2 (liver) human tumor cell lines. 3D-Pharmacophore modeling and quantitative structure-activity relationship (QSAR) analysis were combined to explore the structural requirements controlling the observed anti-tumor properties. It was found that the major structural factors affecting potency of these compounds were related to their basic skeleton. Copyright © 2011 Elsevier Masson SAS. All rights reserved.

  10. Rational design of methicillin resistance staphylococcus aureus inhibitors through 3D-QSAR, molecular docking and molecular dynamics simulations.

    Science.gov (United States)

    Ballu, Srilata; Itteboina, Ramesh; Sivan, Sree Kanth; Manga, Vijjulatha

    2017-12-20

    Staphylococcus aureus is a gram positive bacterium. It is the leading cause of skin and respiratory infections, osteomyelitis, Ritter's disease, endocarditis, and bacteraemia in the developed world. We employed combined studies of 3D QSAR, molecular docking which are validated by molecular dynamics simulations and in silico ADME prediction have been performed on Isothiazoloquinolones inhibitors against methicillin resistance Staphylococcus aureus. Three-dimensional quantitative structure-activity relationship (3D-QSAR) study was applied using comparative molecular field analysis (CoMFA) with Q 2 of 0.578, R 2 of 0.988, and comparative molecular similarity indices analysis (CoMSIA) with Q 2 of 0.554, R 2 of 0.975. The predictive ability of these model was determined using a test set of molecules that gave acceptable predictive correlation (r 2 Pred) values 0.55 and 0.57 of CoMFA and CoMSIA respectively. Docking, simulations were employed to position the inhibitors into protein active site to find out the most probable binding mode and most reliable conformations. Developed models and Docking methods provide guidance to design molecules with enhanced activity. Copyright © 2017 Elsevier Ltd. All rights reserved.

  11. Quantitative structure-activity relationship study of aromatic inhibitors against rat lens aldose reductase activity using variable selections.

    Science.gov (United States)

    Jung, Mankil; Lee, Yongnam; Shim, Minjoo; Lim, Eunyoung; Lee, Eun Jig; Lee, Hyun Chul

    2013-05-01

    A quantitative structure-activity relationship (QSAR) study of aromatic inhibitors against aldose reductase (AR) activity was performed using variable selection from stepwise multiple linear regression (MLR) and genetic algorithm (GA)-MLR. As a result of variable selection, stepwise MLR and GA-MLR gave the same results with one, two, three and five descriptors and different results with four and six descriptors. GA-MLR produced higher values and was better in explanatory and predictive power than stepwise MLR in four variables. AR activity (pIC50) of aromatic derivatives was expressed with acceptable explanatory (74.6-81.2%) and predictive power (68.8-74.4%) in models 3 and 4. The resulting models with the given descriptors illustrate that hydrophobic and electrostatic interactions play a significant role in inhibition of AR activity. This study suggests that the QSAR models can be used as guidelines to predict improved aldose reductase inhibitory activity and to obtain reliable predictions in structurally diverse compounds.

  12. Synthesis, biological evaluation and 3D-QSAR studies of novel 4,5-dihydro-1H-pyrazole niacinamide derivatives as BRAF inhibitors.

    Science.gov (United States)

    Li, Cui-Yun; Li, Qing-Shan; Yan, Li; Sun, Xiao-Guang; Wei, Ran; Gong, Hai-Bin; Zhu, Hai-Liang

    2012-06-15

    A series of novel 4,5-dihydropyrazole derivatives containing niacinamide moiety as potential V600E mutant BRAF kinase (BRAF(V600E)) inhibitors were designed and synthesized. Results of the bioassays against BRAF(V600E) and WM266.4 human melanoma cell line showed several compounds to be endowed potent activities with IC(50) and GI(50) value in low micromolar range, among which compound 27e, (5-(4-Chlorophenyl)-3-(4-methoxyphenyl)-4,5-dihydro-1H-pyrazol-1-yl)6-methylpyridin-3-yl methanone (IC(50)=0.20 μM, GI(50)=0.89 μM) was bearing the best bioactivity comparable with the positive control Sorafenib. Docking simulation was performed to determine the probable binding model and 3D-QSAR model was built to provide more pharmacophore understanding that could use to design new agents with more potent BRAF(V600E) inhibitory activity. Copyright © 2012 Elsevier Ltd. All rights reserved.

  13. Design, biological evaluation and 3D QSAR studies of novel dioxin-containing pyrazoline derivatives with thiourea skeleton as selective HER-2 inhibitors

    Science.gov (United States)

    Yang, Bing; Yang, Yu-Shun; Yang, Na; Li, Guigen; Zhu, Hai-Liang

    2016-06-01

    A series of novel dioxin-containing pyrazoline derivatives with thiourea skeleton have been designed, synthesized and evaluated for their EGFR/HER-2 inhibitory and anti-proliferation activities. A majority of them displayed selective HER-2 inhibitory activity against EGFR inhibitory activity. Compound C20 displayed the most potent activity against HER-2 and MDA-MB-453 human breast cancer cell line (IC50 = 0.03 μM and GI50 = 0.15 μM), being slightly more potent than the positive control Erlotinib (IC50 = 0.16 μM and GI50 = 1.56 μM) and comparable with Lapatinib (IC50 = 0.01 μM and GI50 = 0.03 μM). It is a more exciting result that C20 was over 900 times more potent against HER-2 than against EGFR while this value was 0.19 for Erlotinib and 1.00 for Lapatinib, indicating high selectivity. The results of docking simulation indicate that the dioxin moiety occupied the exit of the active pocket and pushed the carbothioamide deep into the active site. QSAR models have been built with activity data and binding conformations to begin our work in this paper as well as to provide a reliable tool for reasonable design of EGFR/HER-2 inhibitors in future.

  14. Quantitative structure-activity relationships for green algae growth inhibition by polymer particles.

    NARCIS (Netherlands)

    Nolte, Tom M; Peijnenburg, Willie J G M; Hendriks, A Jan; van de Meent, Dik

    After use and disposal of chemical products, many types of polymer particles end up in the aquatic environment with potential toxic effects to primary producers like green algae. In this study, we have developed Quantitative Structure-Activity Relationships (QSARs) for a set of highly structural

  15. Molecular modeling and structure-activity relationship of podophyllotoxin and its congeners.

    Science.gov (United States)

    Naik, Pradeep Kumar; Alam, Afroz; Malhotra, Ashutosh; Rizvi, Owasis

    2010-06-01

    A quantitative structure-activity relationship (QSAR) model has been developed between cytotoxic activity and structural properties by considering a data set of 119 podophyllotoxin analogs based on 2D and 3D structural descriptors. A systematic stepwise searching approach of zero tests, a missing value test, a simple correlation test, a multicollinearity test, and a genetic algorithm method of variable selection was used to generate the model. A statistically significant model (r(train)(2) = 0.906; q(cv)(2) = 0.893) was obtained with the molecular descriptors. The robustness of the QSAR model was characterized by the values of the internal leave-one-out cross-validated regression coefficient (q(cv)(2)) for the training set and r(test)(2) for the test set. The overall root mean square error (RMSE) between the experimental and predicted pIC(50) value was 0.265 and r(test)(2) = 0.824, revealing good predictability of the QSAR model. For an external data set of 16 podophyllotoxin analogs, the QSAR model was able to predict the tubulin polymerization inhibition and mechanistically cytotoxic activity with an RMSE value of 0.295 in comparison to experimental values. The QSAR model developed in this study shall aid further design of novel potent podophyllotoxin derivatives.

  16. QUANTITATIVE ELECTRONIC STRUCTURE - ACTIVITY RELATIONSHIPS ANALYSIS ANTIMUTAGENIC BENZALACETONE DERIVATIVES BY PRINCIPAL COMPONENT REGRESSION APPROACH

    Directory of Open Access Journals (Sweden)

    Yuliana Yuliana

    2010-06-01

    Full Text Available Quantitative Electronic Structure Activity Relationship (QSAR analysis of a series of benzalacetones has been investigated based on semi empirical PM3 calculation data using Principal Components Regression (PCR. Investigation has been done based on antimutagen activity from benzalacetone compounds (presented by log 1/IC50 and was studied as linear correlation with latent variables (Tx resulted from transformation of atomic net charges using Principal Component Analysis (PCA. QSAR equation was determinated based on distribution of selected components and then was analysed with PCR. The result was described by the following QSAR equation : log 1/IC50 = 6.555 + (2.177.T1 + (2.284.T2 + (1.933.T3 The equation was significant on the 95% level with statistical parameters : n = 28 r = 0.766  SE  = 0.245  Fcalculation/Ftable = 3.780 and gave the PRESS result 0.002. It means that there were only a relatively few deviations between the experimental and theoretical data of antimutagenic activity.          New types of benzalacetone derivative compounds were designed  and their theoretical activity were predicted based on the best QSAR equation. It was found that compounds number 29, 30, 31, 32, 33, 35, 36, 37, 38, 40, 41, 42, 44, 47, 48, 49 and 50  have  a relatively high antimutagenic activity.   Keywords: QSAR; antimutagenic activity; benzalaceton; atomic net charge

  17. Exploration of Novel Inhibitors for Class I Histone Deacetylase Isoforms by QSAR Modeling and Molecular Dynamics Simulation Assays.

    Directory of Open Access Journals (Sweden)

    Zainab Noor

    Full Text Available Histone deacetylases (HDAC are metal-dependent enzymes and considered as important targets for cell functioning. Particularly, higher expression of class I HDACs is common in the onset of multiple malignancies which results in deregulation of many target genes involved in cell growth, differentiation and survival. Although substantial attempts have been made to control the irregular functioning of HDACs by employing various inhibitors with high sensitivity towards transformed cells, limited success has been achieved in epigenetic cancer therapy. Here in this study, we used ligand-based pharmacophore and 2-dimensional quantitative structure activity relationship (QSAR modeling approaches for targeting class I HDAC isoforms. Pharmacophore models were generated by taking into account the known IC50 values and experimental energy scores with extensive validations. The QSAR model having an external R2 value of 0.93 was employed for virtual screening of compound libraries. 10 potential lead compounds (C1-C10 were short-listed having strong binding affinities for HDACs, out of which 2 compounds (C8 and C9 were able to interact with all members of class I HDACs. The potential binding modes of HDAC2 and HDAC8 to C8 were explored through molecular dynamics simulations. Overall, bioactivity and ligand efficiency (binding energy/non-hydrogen atoms profiles suggested that proposed hits may be more effective inhibitors for cancer therapy.

  18. Growth Inhibition and DNA Damage Induced by X-Phenols in Yeast: A Quantitative Structure-Activity Relationship Study.

    Science.gov (United States)

    Negritto, M Cristina; Valdez, Clarissa; Sharma, Jasmine; Rosenberg, Christa; Selassie, Cynthia R

    2017-12-31

    Phenolic compounds and their derivatives are ubiquitous constituents of numerous synthetic and natural chemicals that exist in the environment. Their toxicity is mostly attributed to their hydrophobicity and/or the formation of free radicals. In a continuation of the study of phenolic toxicity in a systematic manner, we have examined the biological responses of Saccharomyces cerevisiae to a series of mostly monosubstituted phenols utilizing a quantitative structure-activity relationship (QSAR) approach. The biological end points included a growth assay that determines the levels of growth inhibition induced by the phenols as well as a yeast deletion (DEL) assay that assesses the ability of X-phenols to induce DNA damage or DNA breaks. The QSAR analysis of cell growth patterns determined by IC50 and IC80 values indicates that toxicity is delineated by a hydrophobic, parabolic model. The DEL assay was then utilized to detect genomic deletions in yeast. The increase in the genotoxicity was enhanced by the electrophilicity of the phenolic substituents that were strong electron donors as well as by minimal hydrophobicity. The electrophilicities are represented by Brown's sigma plus values that are a variant of the Hammett sigma constants. A few mutant strains of genes involved in DNA repair were separately exposed to 2,6-di-tert-butyl-4-methyl-phenol (BHT) and butylated hydroxy anisole (BHA). They were subsequently screened for growth phenotypes. BHA-induced growth defects in most of the DNA repair null mutant strains, whereas BHT was unresponsive.

  19. Design of 2-Nitroimidazooxazine Derivatives as Deazaflavin-Dependent Nitroreductase (Ddn) Activators as Anti-Mycobacterial Agents Based on 3D QSAR, HQSAR, and Docking Study with In Silico Prediction of Activity and Toxicity.

    Science.gov (United States)

    Gupta, Nirzari; Vyas, Vivek K; Patel, Bhumika D; Ghate, Manjunath

    2017-09-11

    Deazaflavin-dependent nitroreductase (Ddn) is an emerging target in the field of anti-tuberculosis agents. In the present study, 2-nitroimidazooxazine derivatives as Ddn activators were aligned for CoMFA, CoMSIA and HQSAR analysis. The best CoMFA and CoMSIA model were generated with leave-one-out correlation coefficients (q (2)) of 0.585 and 0.571, respectively. Both the CoMFA and CoMSIA models were also validated by a test set of 11 compounds with satisfactory [Formula: see text] value of 0.701 and 0.667, respectively. Results of 3D QSAR and HQSAR study were used for the designing of novel and potent nitroimidazooxazine derivatives as Ddn activators. 21 novel compounds were designed, and docked into the Ddn enzyme. In docking study compound ng11 showed interaction with key amino acid residues such as Tyr65 and Tyr133, and also showed better ADMET compatibility. The ADMET prediction, docking study and the predicted activity of novel designed compounds revealed that compound ng11 showed good potential as Ddn activators for the treatment of tuberculosis.

  20. Metabolic biotransformation half-lives in fish: QSAR modeling and consensus analysis.

    Science.gov (United States)

    Papa, Ester; van der Wal, Leon; Arnot, Jon A; Gramatica, Paola

    2014-02-01

    Bioaccumulation in fish is a function of competing rates of chemical uptake and elimination. For hydrophobic organic chemicals bioconcentration, bioaccumulation and biomagnification potential are high and the biotransformation rate constant is a key parameter. Few measured biotransformation rate constant data are available compared to the number of chemicals that are being evaluated for bioaccumulation hazard and for exposure and risk assessment. Three new Quantitative Structure-Activity Relationships (QSARs) for predicting whole body biotransformation half-lives (HLN) in fish were developed and validated using theoretical molecular descriptors that seek to capture structural characteristics of the whole molecule and three data set splitting schemes. The new QSARs were developed using a minimal number of theoretical descriptors (n=9) and compared to existing QSARs developed using fragment contribution methods that include up to 59 descriptors. The predictive statistics of the models are similar thus further corroborating the predictive performance of the different QSARs; Q(2)ext ranges from 0.75 to 0.77, CCCext ranges from 0.86 to 0.87, RMSE in prediction ranges from 0.56 to 0.58. The new QSARs provide additional mechanistic insights into the biotransformation capacity of organic chemicals in fish by including whole molecule descriptors and they also include information on the domain of applicability for the chemical of interest. Advantages of consensus modeling for improving overall prediction and minimizing false negative errors in chemical screening assessments, for identifying potential sources of residual error in the empirical HLN database, and for identifying structural features that are not well represented in the HLN dataset to prioritize future testing needs are illustrated. © 2013.

  1. QSAR study of anthranilic acid sulfonamides as inhibitors of methionine aminopeptidase-2 using LS-SVM and GRNN based on principal components.

    Science.gov (United States)

    Shahlaei, Mohsen; Sabet, Razieh; Ziari, Maryam Bahman; Moeinifard, Behzad; Fassihi, Afshin; Karbakhsh, Reza

    2010-10-01

    Quantitative relationships between molecular structure and methionine aminopeptidase-2 inhibitory activity of a series of cytotoxic anthranilic acid sulfonamide derivatives were discovered. We have demonstrated the detailed application of two efficient nonlinear methods for evaluation of quantitative structure-activity relationships of the studied compounds. Components produced by principal component analysis as input of developed nonlinear models were used. The performance of the developed models namely PC-GRNN and PC-LS-SVM were tested by several validation methods. The resulted PC-LS-SVM model had a high statistical quality (R(2)=0.91 and R(CV)(2)=0.81) for predicting the cytotoxic activity of the compounds. Comparison between predictability of PC-GRNN and PC-LS-SVM indicates that later method has higher ability to predict the activity of the studied molecules. Copyright (c) 2010 Elsevier Masson SAS. All rights reserved.

  2. Synthesis, screening and QSAR studies of 3-benzoyl-2-oxo/thioxo-1,2,3,4-tetrahydro-pyrimidine analogues as antibacterial agents

    Directory of Open Access Journals (Sweden)

    Ramesh L. Sawant

    2008-12-01

    Full Text Available The purpose of this study was to synthesize several 3-benzoyl-5-acyl-6-methyl-4-substituted-2-oxo/thioxo-1,2,3,4-tetrahydropyrimidines, evaluate them for their antibacterial activity and to establish correlation between the activity and physicochemical properties. 5-Acyl-6-methyl-4-substituted-2-oxo/thioxo-1,2,3,4-tetrahydropyrimidines (A were synthesized by cyclocondensation reaction between appropriate aldehyde, acetoacetate and urea/thiourea in presence of aluminium chloride and hydrochloric acid which upon treatment with benzoyl chloride in presence of pyridine in benzene furnish the title compounds (1-28. The structures of all title compounds have been confirmed on the basis of their analytical, IR and NMR spectral data. The title compounds have been tested for antibacterial activity against Staphylococcus aureus. The compounds were divided into training and test sets. A quantitative structure activity relationship study was made using various descriptors. Several statistical expressions were developed using stepwise multiple linear regression analysis. The best quantitative structure activity relationship model was further cross validated. The study revealed that total positive partial charge (PC+ and total polar negative Van der Waals surface area (Q_VSA_PNEG contributes negatively where as contribution of Van der Waals surface area to molar refractivity (SMR_VSA7 contributes positively to the antibacterial activity. The compounds with improved antibacterial potential can be successfully designed with selected quantitative structure activity relationship model.

  3. Parallel screening of drug-like natural compounds using Caco-2 cell permeability QSAR model with applicability domain, lipophilic ligand efficiency index and shape property: A case study of HIV-1 reverse transcriptase inhibitors

    Science.gov (United States)

    Patel, Rikin D.; Kumar, Sivakumar Prasanth; Patel, Chirag N.; Shankar, Shetty Shilpa; Pandya, Himanshu A.; Solanki, Hitesh A.

    2017-10-01

    The traditional drug design strategy centrally focuses on optimizing binding affinity with the receptor target and evaluates pharmacokinetic properties at a later stage which causes high rate of attrition in clinical trials. Alternatively, parallel screening allows evaluation of these properties and affinity simultaneously. In a case study to identify leads from natural compounds with experimental HIV-1 reverse transcriptase (RT) inhibition, we integrated various computational approaches including Caco-2 cell permeability QSAR model with applicability domain (AD) to recognize drug-like natural compounds, molecular docking to study HIV-1 RT interactions and shape similarity analysis with known crystal inhibitors having characteristic butterfly-like model. Further, the lipophilic properties of the compounds refined from the process with best scores were examined using lipophilic ligand efficiency (LLE) index. Seven natural compound hits viz. baicalien, (+)-calanolide A, mniopetal F, fagaronine chloride, 3,5,8-trihydroxy-4-quinolone methyl ether derivative, nitidine chloride and palmatine, were prioritized based on LLE score which demonstrated Caco-2 well absorption labeling, encompassment in AD structural coverage, better receptor affinity, shape adaptation and permissible AlogP value. We showed that this integrative approach is successful in lead exploration of natural compounds targeted against HIV-1 RT enzyme.

  4. A Review of Recent Advances towards the Development of (Quantitative Structure-Activity Relationships for Metallic Nanomaterials

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    Guangchao Chen

    2017-08-01

    Full Text Available Gathering required information in a fast and inexpensive way is essential for assessing the risks of engineered nanomaterials (ENMs. The extension of conventional (quantitative structure-activity relationships ((QSARs approach to nanotoxicology, i.e., nano-(QSARs, is a possible solution. The preliminary attempts of correlating ENMs’ characteristics to the biological effects elicited by ENMs highlighted the potential applicability of (QSARs in the nanotoxicity field. This review discusses the current knowledge on the development of nano-(QSARs for metallic ENMs, on the aspects of data sources, reported nano-(QSARs, and mechanistic interpretation. An outlook is given on the further development of this frontier. As concluded, the used experimental data mainly concern the uptake of ENMs by different cell lines and the toxicity of ENMs to cells lines and Escherichia coli. The widely applied techniques of deriving models are linear and non-linear regressions, support vector machine, artificial neural network, k-nearest neighbors, etc. Concluded from the descriptors, surface properties of ENMs are seen as vital for the cellular uptake of ENMs; the capability of releasing ions and surface redox properties of ENMs are of importance for evaluating nanotoxicity. This review aims to present key advances in relevant nano-modeling studies and stimulate future research efforts in this quickly developing field of research.

  5. QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP ANALYSIS OF CURCUMIN AND ITS DERIVATIVES AS GST INHIBITORS BASED ON COMPUTATIONAL CHEMISTRY CALCULATION

    Directory of Open Access Journals (Sweden)

    Enade Perdana Istyastono

    2010-06-01

    Full Text Available The Quantitative Structure-Activity Relationship (QSAR study was established on curcumin and its derivatives as glutathione S-transferase(s (GSTs inhibitors using atomic net charges as the descriptors. The charges were resulted by semiempirical AM1 and PM3 quantum-chemical calculations using computational chemistry approach. The inhibition activity was expressed as the concentration that gave 50% inhibition of GSTs activity (IC50. The selection of the best QSAR equation models was determined by multiple linear regression analysis. This research was related to the nature of GSTs as multifunctional enzymes, which play an important role in the detoxification of electrophilic compounds, the process of inflammation and the effectivity of anticancer compounds. The result showed that AM1 semiempirical method gave better descriptor for the construction of QSAR equation model than PM3 did. The best QSAR equation model was described by : log 1/IC50 = -2,238 - 17,326 qC2' + 1,876 qC4' + 9,200 qC6' The equation was significant at 95% level with statistical parameters : n = 10, m = 3, r­ = 0,839, SE = 0,254, F = 4,764, F/Ftable = 1,001.   Keywords: QSAR analysis, curcumin, glutathione S-transferase(s (GSTs, atomic net charge

  6. Critical body residues linked to octanol-water partitioning, organism composition, and LC50 QSARs: Meta-analysis and model

    NARCIS (Netherlands)

    Hendriks, A.J.; Traas, T.P.; Huijbregts, M.A.J.

    2005-01-01

    To protect thousands of species from thousands of chemicals released in the environment, various risk assessment tools have been developed. Here, we link quantitative structure-activity relationships (QSARs) for response concentrations in water (LC50) to critical concentrations in organisms (C-50)

  7. Web-4D-QSAR: A web-based application to generate 4D-QSAR descriptors.

    Science.gov (United States)

    Ataide Martins, João Paulo; Rougeth de Oliveira, Marco Antônio; Oliveira de Queiroz, Mário Sérgio

    2018-02-05

    A web-based application is developed to generate 4D-QSAR descriptors using the LQTA-QSAR methodology, based on molecular dynamics (MD) trajectories and topology information retrieved from the GROMACS package. The LQTAGrid module calculates the intermolecular interaction energies at each grid point, considering probes and all aligned conformations resulting from MD simulations. These interaction energies are the independent variables or descriptors employed in a QSAR analysis. A friendly front end web interface, built using the Django framework and Python programming language, integrates all steps of the LQTA-QSAR methodology in a way that is transparent to the user, and in the backend, GROMACS and LQTAGrid are executed to generate 4D-QSAR descriptors to be used later in the process of QSAR model building. © 2018 Wiley Periodicals, Inc.

  8. Adaptive neuro-fuzzy inference system (ANFIS): a new approach to predictive modeling in QSAR applications: a study of neuro-fuzzy modeling of PCP-based NMDA receptor antagonists.

    Science.gov (United States)

    Buyukbingol, Erdem; Sisman, Arzu; Akyildiz, Murat; Alparslan, Ferda Nur; Adejare, Adeboye

    2007-06-15

    This paper proposes a new method, Adaptive Neuro-Fuzzy Inference System (ANFIS) to evaluate physicochemical descriptors of certain chemical compounds for their appropriate biological activities in terms of QSAR models with the aid of artificial neural network (ANN) approach combined with the principle of fuzzy logic. The ANFIS was utilized to predict NMDA (N-methyl-d-Aspartate) receptor binding activities of phencyclidine (PCP) derivatives. A data set of 38 drug-like compounds was coded with 1244 calculated molecular structure descriptors (clustered in 20 data sets) which were obtained from several sources, mainly from Dragon software. Prior to the progress to the ANFIS system, descriptors from the best subsets were selected using unsupervised forward selection (UFS) to eliminate redundancy and multicollinearity followed by fuzzy linear regression algorithm (FLR) which was used for variable selection. ANFIS was applied to train the final descriptors (Mor22m, E3s, R3v+, and R1e+) using a hybrid algorithm consisting of back-propagation and least-square estimation while the optimum number and shape of related functions were obtained through the subtractive clustering algorithm. Comparison of the proposed method with traditional methods, that is, multiple linear regression (MLR) and partial least-square (PLS) was also studied and the results indicated that the ANFIS model obtained from data sets achieved satisfactory accuracy.

  9. 3D QSAR modeling of 4-nerolidylcatechol derivatives and virtual screening for identification of potent plasmodium inhibitor

    Directory of Open Access Journals (Sweden)

    Dhrubajyoti Gogoi

    2014-08-01

    Full Text Available The present study was aim to develop a three dimensional quantitative structure–activity relationships (3D QSAR model based on the structure of 4-nerolidylcatechol (IC50=0.67 µM, a novel plant derived Plasmodium inhibitor and its derivatives for identification of efficient antimalarial lead. A statisti-cally validated Partial Least-Squares (PLS based Molecular Field Analysis (MFA model was built up using the training set of eight 4-nerolidylcatechol derivatives and their diverse conformers. A statistically reliable model with good predictive power (cross-validated correlation coefficient q2=0.769 was obtained. Hence, the generated model was used to screen a library of 30,000 compounds of chembridge database (http://www.chembridge.com. Results of drug likeness prediction and ADMET study has suggested six compounds as potential antimalarial/plasmodial lead.

  10. Molecular docking and QSAR of aplyronine A and analogues: potent inhibitors of actin

    Science.gov (United States)

    Hussain, Abrar; Melville, James L.; Hirst, Jonathan D.

    2010-01-01

    Actin-binding natural products have been identified as a potential basis for the design of cancer therapeutic agents. We report flexible docking and QSAR studies on aplyronine A analogues. Our findings show the macrolide `tail' to be fundamental for the depolymerisation effect of actin-binding macrolides and that it is the tail which forms the initial interaction with the actin rather than the macrocycle, as previously believed. Docking energy scores for the compounds were highly correlated with actin depolymerisation activity. The 3D-QSAR models were predictive, with the best model giving a q 2 value of 0.85 and a r 2 of 0.94. Results from the docking simulations and the interpretation from QSAR "coeff*stdev" contour maps provide insight into the binding mechanism of each analogue and highlight key features that influence depolymerisation activity. The results herein may aid the design of a putative set of analogues that can help produce efficacious and tolerable anti-tumour agents. Finally, using the best QSAR model, we have also made genuine predictions for an independent set of recently reported aplyronine analogues.

  11. Topological study on the toxicity of ionic liquids on Vibrio fischeri by the quantitative structure-activity relationship method.

    Science.gov (United States)

    Yan, Fangyou; Shang, Qiaoyan; Xia, Shuqian; Wang, Qiang; Ma, Peisheng

    2015-04-09

    As environmentally friendly solvents, ionic liquids (ILs) are unlikely to act as air contaminants or inhalation toxins resulting from their negligible vapor pressure and excellent thermal stability. However, they can be potential water contaminants because of their considerable solubility in water; therefore, a proper toxicological assessment of ILs is essential. The environmental fate of ILs is studied by quantitative structure-activity relationship (QSAR) method. A multiple linear regression (MLR) model is obtained by topological method using toxicity data of 157 ILs on Vibrio fischeri, which are composed of 74 cations and 22 anions. The topological index developed in our research group is used for predicting the V. fischeri toxicity for the first time. The MLR model is precise for estimating LogEC50 of ILs on V. fischeri with square of correlation coefficient (R(2)) = 0.908 and the average absolute error (AAE) = 0.278. Copyright © 2015 Elsevier B.V. All rights reserved.

  12. Quantitative structure activity relationship study of anticonvulsant activity of α_substituted acetamido-N-benzylacetamide derivatives

    Directory of Open Access Journals (Sweden)

    Usman Abdulfatai

    2016-12-01

    Full Text Available To develop the quantitative structure–activity relationship (QSAR for predicting the anticonvulsant activity of α_substituted acetamido-N-benzylacetamide derivatives. Density Functional Theory (B3LYP/6-31G* quantum chemical calculation method was used to find the optimized geometry of the studied molecules. Nine types of molecular descriptors were used to derive a quantitative relation between anticonvulsant activity and structural properties. The relevant molecular descriptors were selected by genetic algorithm approximation. The high value of the correlation coefficient, (R2 of 0.98, indicates that the model was satisfactory. The proposed model has good stability, robustness, and predictability on verifying with internal and external validation.

  13. Mechanistic QSAR models for interpreting degradation rates of sulfonamides in UV-photocatalysis systems.

    Science.gov (United States)

    Huang, Xiangfeng; Feng, Yi; Hu, Cui; Xiao, Xiaoyu; Yu, Daliang; Zou, Xiaoming

    2015-11-01

    Photocatalysis is one of the most effective methods for treating antibiotic wastewater. Thus, it is of great significance to determine the relationship between degradation rates and structural characteristics of antibiotics in photocatalysis processes. In the present study, the photocatalytic degradation characteristics of 10 sulfonamides (SAs) were studied using two photocatalytic systems composed of nanophase titanium dioxide (nTiO2) plus ultraviolet (UV) and nTiO2/activated carbon fiber (ACF) plus UV. The results indicated that the largest apparent SA degradation rate constant (Kapp) is approximately 5 times as large as that of the smallest one. Based on the degradation mechanism and the partial least squares regression (PLS) method, optimum Quantitative Structure Activity Relationship (QSAR) models were developed for the two systems. Mechanistic models indicated that the degradation rule of SAs in the TiO2 systems strongly relates to their highest occupied molecular orbital (Ehomo), the maximum values of nucleophilic attack (f(+)x), and the minimum values of the most negative partial charge on a main-chain atom (q(C)min), whereas the maximum values of OH radical attack (f(0)x) and the apparent adsorption rate constant values (kad) are key factors affecting the degradation rule of SAs in the TiO2/ACF system. Copyright © 2015 Elsevier Ltd. All rights reserved.

  14. QSAR Analysis of 2-Amino or 2-Methyl-1-Substituted Benzimidazoles Against Pseudomonas aeruginosa

    Directory of Open Access Journals (Sweden)

    Dijana J. Barna

    2009-04-01

    Full Text Available A set of benzimidazole derivatives were tested for their inhibitory activities against the Gram-negative bacterium Pseudomonas aeruginosa and minimum inhibitory concentrations were determined for all the compounds. Quantitative structure activity relationship (QSAR analysis was applied to fourteen of the abovementioned derivatives using a combination of various physicochemical, steric, electronic, and structural molecular descriptors. A multiple linear regression (MLR procedure was used to model the relationships between molecular descriptors and the antibacterial activity of the benzimidazole derivatives. The stepwise regression method was used to derive the most significant models as a calibration model for predicting the inhibitory activity of this class of molecules. The best QSAR models were further validated by a leave one out technique as well as by the calculation of statistical parameters for the established theoretical models. To confirm the predictive power of the models, an external set of molecules was used. High agreement between experimental and predicted inhibitory values, obtained in the validation procedure, indicated the good quality of the derived QSAR models.

  15. Antiplasmodial Activity, Cytotoxicity and Structure-Activity Relationship Study of Cyclopeptide Alkaloids

    Directory of Open Access Journals (Sweden)

    Emmy Tuenter

    2017-02-01

    Full Text Available Cyclopeptide alkaloids are polyamidic, macrocyclic compounds, containing a 13-, 14-, or 15-membered ring. The ring system consists of a hydroxystyrylamine moiety, an amino acid, and a β-hydroxy amino acid; attached to the ring is a side chain, comprised of one or two more amino acid moieties. In vitro antiplasmodial activity was shown before for several compounds belonging to this class, and in this paper the antiplasmodial and cytotoxic activities of ten more cyclopeptide alkaloids are reported. Combining these results and the IC50 values that were reported by our group previously, a library consisting of 19 cyclopeptide alkaloids was created. A qualitative SAR (structure-activity relationship study indicated that a 13-membered macrocyclic ring is preferable over a 14-membered one. Furthermore, the presence of a β-hydroxy proline moiety could correlate with higher antiplasmodial activity, and methoxylation (or, to a lesser extent, hydroxylation of the styrylamine moiety could be important for displaying antiplasmodial activity. In addition, QSAR (quantitative structure-activity relationship models were developed, using PLS (partial least squares regression and MLR (multiple linear regression. On the one hand, these models allow for the indication of the most important descriptors (molecular properties responsible for the antiplasmodial activity. Additionally, predictions made for interesting structures did not contradict the expectations raised in the qualitative SAR study.

  16. Antiplasmodial Activity, Cytotoxicity and Structure-Activity Relationship Study of Cyclopeptide Alkaloids.

    Science.gov (United States)

    Tuenter, Emmy; Segers, Karen; Kang, Kyo Bin; Viaene, Johan; Sung, Sang Hyun; Cos, Paul; Maes, Louis; Heyden, Yvan Vander; Pieters, Luc

    2017-02-02

    Cyclopeptide alkaloids are polyamidic, macrocyclic compounds, containing a 13-, 14-, or 15-membered ring. The ring system consists of a hydroxystyrylamine moiety, an amino acid, and a β-hydroxy amino acid; attached to the ring is a side chain, comprised of one or two more amino acid moieties. In vitro antiplasmodial activity was shown before for several compounds belonging to this class, and in this paper the antiplasmodial and cytotoxic activities of ten more cyclopeptide alkaloids are reported. Combining these results and the IC50 values that were reported by our group previously, a library consisting of 19 cyclopeptide alkaloids was created. A qualitative SAR (structure-activity relationship) study indicated that a 13-membered macrocyclic ring is preferable over a 14-membered one. Furthermore, the presence of a β-hydroxy proline moiety could correlate with higher antiplasmodial activity, and methoxylation (or, to a lesser extent, hydroxylation) of the styrylamine moiety could be important for displaying antiplasmodial activity. In addition, QSAR (quantitative structure-activity relationship) models were developed, using PLS (partial least squares regression) and MLR (multiple linear regression). On the one hand, these models allow for the indication of the most important descriptors (molecular properties) responsible for the antiplasmodial activity. Additionally, predictions made for interesting structures did not contradict the expectations raised in the qualitative SAR study.

  17. 3D-QSAR and Molecular Docking Studies on the TcPMCA1-Mediated Detoxification of Scopoletin and Coumarin Derivatives

    Directory of Open Access Journals (Sweden)

    Qiu-Li Hou

    2017-06-01

    Full Text Available The carmine spider mite, Tetranychus cinnabarinus (Boisduval, is an economically important agricultural pest that is difficult to prevent and control. Scopoletin is a botanical coumarin derivative that targets Ca2+-ATPase to exert a strong acaricidal effect on carmine spider mites. In this study, the full-length cDNA sequence of a plasma membrane Ca2+-ATPase 1 gene (TcPMCA1 was cloned. The sequence contains an open reading frame of 3750 bp and encodes a putative protein of 1249 amino acids. The effects of scopoletin on TcPMCA1 expression were investigated. TcPMCA1 was significantly upregulated after it was exposed to 10%, 30%, and 50% of the lethal concentration of scopoletin. Homology modeling, molecular docking, and three-dimensional quantitative structure-activity relationships were then studied to explore the relationship between scopoletin structure and TcPMCA1-inhibiting activity of scopoletin and other 30 coumarin derivatives. Results showed that scopoletin inserts into the binding cavity and interacts with amino acid residues at the binding site of the TcPMCA1 protein through the driving forces of hydrogen bonds. Furthermore, CoMFA (comparative molecular field analysis- and CoMSIA (comparative molecular similarity index analysis-derived models showed that the steric and H-bond fields of these compounds exert important influences on the activities of the coumarin compounds.Notably, the C3, C6, and C7 positions in the skeletal structure of the coumarins are the most suitable active sites. This work provides insights into the mechanism underlying the interaction of scopoletin with TcPMCA1. The present results can improve the understanding on plasma membrane Ca2+-ATPase-mediated (PMCA-mediated detoxification of scopoletin and coumarin derivatives in T. cinnabarinus, as well as provide valuable information for the design of novel PMCA-inhibiting acaricides.

  18. Molecular modeling and structure-activity relationships for a series of benzimidazole derivatives as cruzain inhibitors.

    Science.gov (United States)

    Pauli, Ivani; Ferreira, Leonardo G; de Souza, Mariana L; Oliva, Glaucius; Ferreira, Rafaela S; Dessoy, Marco A; Slafer, Brian W; Dias, Luiz C; Andricopulo, Adriano D

    2017-05-01

    Chagas disease is endemic in Latin America and no effective treatment is available. Efforts in drug research have focused on several enzymes from Trypanosoma cruzi, among which cruzain is a validated pharmacological target. Chemometric analyses were performed on the data set using the hologram quantitative structure-activity relationship, comparative molecular field analysis and comparative molecular similarity index analysis methods. Docking simulations were executed using the crystallographic structure of cruzain in complex with a benzimidazole inhibitor. The top-scoring enzyme-inhibitor complexes were selected for the development of the 3D quantitative structure-activity relationship (QSAR) models and to assess the inhibitor binding modes and intermolecular interactions. Benzimidazole derivatives as cruzain inhibitors were used in molecular docking and QSAR studies. Significant statistical indicators were obtained, and the best models demonstrated high predictive ability for an external test set (r 2pred = 0.65, 0.94 and 0.82 for hologram QSAR, comparative molecular field analysis and comparative molecular similarity index analysis, respectively). Additionally, the graphical information of the chemometric analyses demonstrated substantial complementarity with the enzyme-binding site. These results demonstrate the relevance of the QSAR models to guide the design of structurally related benzimidazole derivatives with improved potency.

  19. Synthesis and 2D-QSAR studies of neolignan-based diaryl-tetrahydrofuran and -furan analogues with remarkable activity against Trypanosoma cruzi and assessment of the trypanothione reductase activity.

    Science.gov (United States)

    Hartmann, Ana Paula; de Carvalho, Marcelo Rodrigues; Bernardes, Lilian Sibelle Campos; Moraes, Milena Hoehr de; de Melo, Eduardo Borges; Lopes, Carla Duque; Steindel, Mario; da Silva, João Santana; Carvalho, Ivone

    2017-11-10

    Two series of diaryl-tetrahydrofuran and -furan were synthesised and screened for anti-trypanosomal activity against trypomastigote and amastigote forms of Trypanosoma cruzi, the causative agent of Chagas disease. Based on evidence that modification of a natural product may result in a more effective drug than the natural product itself, and using known neolignan inhibitors veraguensin 1 and grandisin 2 as templates to synthesise simpler analogues, remarkable anti-trypanosomal activity and selectivity were found for 3,5-dimethoxylated diaryl-furan 5c and 2,4-dimethoxylated diaryl-tetrahydrofuran 4e analogues with EC 50 0.01 μM and EC 50 0.75 μM, respectively, the former being 260-fold more potent than veraguensin 1 and 150-fold better than benznidazole, the current available drugs for Chagas disease treatment. The ability of the most potent anti-trypanosomal compounds to penetrate LLC-MK2 cells infected with T. cruzi amastigotes parasite was tested, which revealed 4e and 5e analogues as the most effective, causing no damage to mammalian cells. In particular, the majority of the derivatives were non-toxic against mice spleen cells. 2D-QSAR studies show the rigid central core and the position of dimethoxy-aryl substituents dramatically affect the anti-trypanosomal activity. The mode of action of the most active anti-trypanosomal derivatives was investigated by exploring the anti-oxidant functions of Trypanothione reductase (TR). As a result, diarylfuran series displayed the strongest inhibition, highlighting compounds 5d-e (IC 50 19.2 and 17.7 μM) and 5f-g (IC 50 8.9 and 7.4 μM), respectively, with similar or 2-fold higher than the reference inhibitor clomipramine (IC 50 15.2 μM). Copyright © 2017 Elsevier Masson SAS. All rights reserved.

  20. Multi-Layer Identification of Highly-Potent ABCA1 Up-Regulators Targeting LXRβ Using Multiple QSAR Modeling, Structural Similarity Analysis, and Molecular Docking

    Directory of Open Access Journals (Sweden)

    Meimei Chen

    2016-11-01

    Full Text Available In this study, in silico approaches, including multiple QSAR modeling, structural similarity analysis, and molecular docking, were applied to develop QSAR classification models as a fast screening tool for identifying highly-potent ABCA1 up-regulators targeting LXRβ based on a series of new flavonoids. Initially, four modeling approaches, including linear discriminant analysis, support vector machine, radial basis function neural network, and classification and regression trees, were applied to construct different QSAR classification models. The statistics results indicated that these four kinds of QSAR models were powerful tools for screening highly potent ABCA1 up-regulators. Then, a consensus QSAR model was developed by combining the predictions from these four models. To discover new ABCA1 up-regulators at maximum accuracy, the compounds in the ZINC database that fulfilled the requirement of structural similarity of 0.7 compared to known potent ABCA1 up-regulator were subjected to the consensus QSAR model, which led to the discovery of 50 compounds. Finally, they were docked into the LXRβ binding site to understand their role in up-regulating ABCA1 expression. The excellent binding modes and docking scores of 10 hit compounds suggested they were highly-potent ABCA1 up-regulators targeting LXRβ. Overall, this study provided an effective strategy to discover highly potent ABCA1 up-regulators.

  1. Multi-Layer Identification of Highly-Potent ABCA1 Up-Regulators Targeting LXRβ Using Multiple QSAR Modeling, Structural Similarity Analysis, and Molecular Docking.

    Science.gov (United States)

    Chen, Meimei; Yang, Fafu; Kang, Jie; Yang, Xuemei; Lai, Xinmei; Gao, Yuxing

    2016-11-29

    In this study, in silico approaches, including multiple QSAR modeling, structural similarity analysis, and molecular docking, were applied to develop QSAR classification models as a fast screening tool for identifying highly-potent ABCA1 up-regulators targeting LXRβ based on a series of new flavonoids. Initially, four modeling approaches, including linear discriminant analysis, support vector machine, radial basis function neural network, and classification and regression trees, were applied to construct different QSAR classification models. The statistics results indicated that these four kinds of QSAR models were powerful tools for screening highly potent ABCA1 up-regulators. Then, a consensus QSAR model was developed by combining the predictions from these four models. To discover new ABCA1 up-regulators at maximum accuracy, the compounds in the ZINC database that fulfilled the requirement of structural similarity of 0.7 compared to known potent ABCA1 up-regulator were subjected to the consensus QSAR model, which led to the discovery of 50 compounds. Finally, they were docked into the LXRβ binding site to understand their role in up-regulating ABCA1 expression. The excellent binding modes and docking scores of 10 hit compounds suggested they were highly-potent ABCA1 up-regulators targeting LXRβ. Overall, this study provided an effective strategy to discover highly potent ABCA1 up-regulators.

  2. Discovery of unsymmetrical aromatic disulfides as novel inhibitors of SARS-CoV main protease: Chemical synthesis, biological evaluation, molecular docking and 3D-QSAR study.

    Science.gov (United States)

    Wang, Li; Bao, Bo-Bo; Song, Guo-Qing; Chen, Cheng; Zhang, Xu-Meng; Lu, Wei; Wang, Zefang; Cai, Yan; Li, Shuang; Fu, Sheng; Song, Fu-Hang; Yang, Haitao; Wang, Jian-Guo

    2017-09-08

    The worldwide outbreak of severe acute respiratory syndrome (SARS) in 2003 had caused a high rate of mortality. Main protease (Mpro) of SARS-associated coronavirus (SARS-CoV) is an important target to discover pharmaceutical compounds for the therapy of this life-threatening disease. During the course of screening new anti-SARS agents, we have identified that a series of unsymmetrical aromatic disulfides inhibited SARS-CoV Mpro significantly for the first time. Herein, 40 novel unsymmetrical aromatic disulfides were synthesized chemically and their biological activities were evaluated in vitro against SARS-CoV Mpro. These novel compounds displayed excellent IC50 data in the range of 0.516-5.954 μM. Preliminary studies indicated that these disulfides are reversible and mpetitive inhibitors. A possible binding mode was generated via molecular docking simulation and a comparative field analysis (CoMFA) model was constructed to understand the structure-activity relationships. The present research therefore has provided some meaningful guidance to design and identify anti-SARS drugs with totally new chemical structures. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

  3. Quantitative Structure – Antioxidant Activity Relationships of Flavonoid Compounds

    OpenAIRE

    Károly Héberger; Judit Jakus; Orsolya Farkas

    2004-01-01

    A quantitative structure – antioxidant activity relationship (QSAR) study of 36 flavonoids was performed using the partial least squares projection of latent structures (PLS) method. The chemical structures of the flavonoids have been characterized by constitutional descriptors, two-dimensional topological and connectivity indices. Our PLS model gave a proper description and a suitable prediction of the antioxidant activities of a diverse set of flavonoids having clustering tendency....

  4. Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Liying; Sedykh, Alexander; Tripathi, Ashutosh [Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC (United States); Zhu, Hao [The Rutgers Center for Computational and Integrative Biology, Rutgers University, Camden, NJ (United States); Department of Chemistry, Rutgers University, Camden, NJ (United States); Afantitis, Antreas; Mouchlis, Varnavas D.; Melagraki, Georgia [NovaMechanics Ltd., Nicosia (Cyprus); Rusyn, Ivan, E-mail: iir@unc.edu [Department of Environmental Sciences and Engineering, University of North Carolina, Chapel Hill, NC (United States); Tropsha, Alexander, E-mail: alex_tropsha@unc.edu [Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC (United States)

    2013-10-01

    Identification of endocrine disrupting chemicals is one of the important goals of environmental chemical hazard screening. We report on the development of validated in silico predictors of chemicals likely to cause estrogen receptor (ER)-mediated endocrine disruption to facilitate their prioritization for future screening. A database of relative binding affinity of a large number of ERα and/or ERβ ligands was assembled (546 for ERα and 137 for ERβ). Both single-task learning (STL) and multi-task learning (MTL) continuous quantitative structure–activity relationship (QSAR) models were developed for predicting ligand binding affinity to ERα or ERβ. High predictive accuracy was achieved for ERα binding affinity (MTL R{sup 2} = 0.71, STL R{sup 2} = 0.73). For ERβ binding affinity, MTL models were significantly more predictive (R{sup 2} = 0.53, p < 0.05) than STL models. In addition, docking studies were performed on a set of ER agonists/antagonists (67 agonists and 39 antagonists for ERα, 48 agonists and 32 antagonists for ERβ, supplemented by putative decoys/non-binders) using the following ER structures (in complexes with respective ligands) retrieved from the Protein Data Bank: ERα agonist (PDB ID: 1L2I), ERα antagonist (PDB ID: 3DT3), ERβ agonist (PDB ID: 2NV7), and ERβ antagonist (PDB ID: 1L2J). We found that all four ER conformations discriminated their corresponding ligands from presumed non-binders. Finally, both QSAR models and ER structures were employed in parallel to virtually screen several large libraries of environmental chemicals to derive a ligand- and structure-based prioritized list of putative estrogenic compounds to be used for in vitro and in vivo experimental validation. - Highlights: • This is the largest curated dataset inclusive of ERα and β (the latter is unique). • New methodology that for the first time affords acceptable ERβ models. • A combination of QSAR and docking enables prediction of affinity and function.

  5. QSAR modeling based on structure-information for properties of interest in human health.

    Science.gov (United States)

    Hall, L H; Hall, L M

    2005-01-01

    The development of QSAR models based on topological structure description is presented for problems in human health. These models are based on the structure-information approach to quantitative biological modeling and prediction, in contrast to the mechanism-based approach. The structure-information approach is outlined, starting with basic structure information developed from the chemical graph (connection table). Information explicit in the connection table (element identity and skeletal connections) leads to significant (implicit) structure information that is useful for establishing sound models of a wide range of properties of interest in drug design. Valence state definition leads to relationships for valence state electronegativity and atom/group molar volume. Based on these important aspects of molecules, together with skeletal branching patterns, both the electrotopological state (E-state) and molecular connectivity (chi indices) structure descriptors are developed and described. A summary of four QSAR models indicates the wide range of applicability of these structure descriptors and the predictive quality of QSAR models based on them: aqueous solubility (5535 chemically diverse compounds, 938 in external validation), percent oral absorption (%OA, 417 therapeutic drugs, 195 drugs in external validation testing), AMES mutagenicity (2963 compounds including 290 therapeutic drugs, 400 in external validation), fish toxicity (92 substituted phenols, anilines and substituted aromatics). These models are established independent of explicit three-dimensional (3-D) structure information and are directly interpretable in terms of the implicit structure information useful to the drug design process.

  6. QSAR Analysis of Benzothiazole Derivatives of Antimalarial Compounds Based On AM1 Semi-Empirical Method

    Directory of Open Access Journals (Sweden)

    Ruslin Hadanu

    2015-03-01

    Full Text Available Quantitative Structure and Activity Relationship (QSAR analysis of 13 benzothiazoles derivatives compound as antimalarial compounds have been performed using electronic descriptor of the atomic net charges (q, dipole moment (μ, ELUMO, EHOMO and polarizability (α. The electronic structures as descriptors were calculated through HyperChem for Windows 7.0 using AM1 semi-empirical method. The descriptors were obtained through molecules modeling to get the most stable structure after geometry optimization step. The antimalarial activity (IC50 were taken from literature. The best model of QSAR model was determined by multiple linear regression approach and giving equation of QSAR: Log IC50 = 23.527 + 4.024 (qC4 + 273.416 (qC5 + 141.663 (qC6 – 0.567 (ELUMO – 3.878 (EHOMO– 2.096 (α. The equation was significant on the 95% level with statistical parameters: n = 13, r = 0.994, r2 = 0.987, SE = 0.094, Fcalc/Ftable = 11.212, and gave the PRESS = 0.348. Its means that there were only a relatively few deviations between the experimental and theoretical data of antimalarial activity.

  7. QSAR analysis of nitroaromatics' toxicity in Tetrahymena pyriformis: structural factors and possible modes of action

    Science.gov (United States)

    Artemenko, A.G.; Muratov, E. N.; Kuz’min, V.E.; Muratov, N.N.; Varlamova, E.V.; Kuz'mina, A.V.; Gorb, L. G.; Golius, A.; Hill, F.C.; Leszczynski, J.; Tropsha, A.

    2012-01-01

    The Hierarchical Technology for Quantitative Structure - Activity Relationships (HiT QSAR) was applied to 95 diverse nitroaromatic compounds (including some widely known explosives) tested for their toxicity (50% inhibition growth concentration, IGC50) against the ciliate Tetrahymena pyriformis. The dataset was divided into subsets according to putative mechanisms of toxicity. Classification and Regression Trees (CART) approach implemented within HiT QSAR has been used for prediction of mechanism of toxicity for new compounds. The resulting models were shown to have ~80% accuracy for external datasets indicating that the mechanistic dataset division was sensible. Then, Partial Least Squares (PLS) statistical approach was used for the development of 2D QSAR models. Validated PLS models were explored to (i) elucidate the effects of different substituents in nitroaromatic compounds on toxicity; (ii) differentiate compounds by probable mechanisms of toxicity based on their structural descriptors; (iii) analyze the role of various physical-chemical factors responsible for compounds’ toxicity. Models were interpreted in terms of molecular fragments promoting or interfering with toxicity. It was also shown that mutual influence of substituents in benzene ring plays the determining role in toxicity variation. Although chemical mechanism based models were statistically significant and externally predictive (R2ext=0.64 for the external set of 63 nitroaromatics identified after all calculations have been completed), they were also shown to have limited coverage (57% for modeling and 76% for external set). PMID:21714735

  8. Brand relationships: A study of five relationship constructs

    DEFF Research Database (Denmark)

    Bergkvist, Lars; Bech-Larsen, Tino

    This study investigates the relationships between the five brand relationship constructs sense of community, brand identification, brand love, brand loyalty, and active engagement. Structural equation modelling with partial least squares showed provided support for six relational hypotheses....... An important finding was that sense of community, a construct that applies to at general consumer context and not only brand communities, is an important antecedent of brand loyalty and brand love. The study also shows that brand love can be more narrowly defined and operationalzed than it has been in previous...

  9. Perspectives from the NanoSafety Modelling Cluster on the validation criteria for (Q)SAR models used in nanotechnology.

    Science.gov (United States)

    Puzyn, Tomasz; Jeliazkova, Nina; Sarimveis, Haralambos; Marchese Robinson, Richard L; Lobaskin, Vladimir; Rallo, Robert; Richarz, Andrea-N; Gajewicz, Agnieszka; Papadopulos, Manthos G; Hastings, Janna; Cronin, Mark T D; Benfenati, Emilio; Fernández, Alberto

    2017-09-21

    Nanotechnology and the production of nanomaterials have been expanding rapidly in recent years. Since many types of engineered nanoparticles are suspected to be toxic to living organisms and to have a negative impact on the environment, the process of designing new nanoparticles and their applications must be accompanied by a thorough risk analysis. (Quantitative) Structure-Activity Relationship ([Q]SAR) modelling creates promising options among the available methods for the risk assessment. These in silico models can be used to predict a variety of properties, including the toxicity of newly designed nanoparticles. However, (Q)SAR models must be appropriately validated to ensure the clarity, consistency and reliability of predictions. This paper is a joint initiative from recently completed European research projects focused on developing (Q)SAR methodology for nanomaterials. The aim was to interpret and expand the guidance for the well-known "OECD Principles for the Validation, for Regulatory Purposes, of (Q)SAR Models", with reference to nano-(Q)SAR, and present our opinions on the criteria to be fulfilled for models developed for nanoparticles. Copyright © 2017. Published by Elsevier Ltd.

  10. QSAR prediction of estrogen activity for a large set of diverse chemicals under the guidance of OECD principles.

    Science.gov (United States)

    Liu, Huanxiang; Papa, Ester; Gramatica, Paola

    2006-11-01

    A large number of environmental chemicals, known as endocrine-disrupting chemicals, are suspected of disrupting endocrine functions by mimicking or antagonizing natural hormones, and such chemicals may pose a serious threat to the health of humans and wildlife. They are thought to act through a variety of mechanisms, mainly estrogen-receptor-mediated mechanisms of toxicity. However, it is practically impossible to perform thorough toxicological tests on all potential xenoestrogens, and thus, the quantitative structure--activity relationship (QSAR) provides a promising method for the estimation of a compound's estrogenic activity. Here, QSAR models of the estrogen receptor binding affinity of a large data set of heterogeneous chemicals have been built using theoretical molecular descriptors, giving full consideration to the new OECD principles in regulation for QSAR acceptability, during model construction and assessment. An unambiguous multiple linear regression (MLR) algorithm was used to build the models, and model predictive ability was validated by both internal and external validation. The applicability domain was checked by the leverage approach to verify prediction reliability. The results obtained using several validation paths indicate that the proposed QSAR model is robust and satisfactory, and can provide a feasible and practical tool for the rapid screening of the estrogen activity of organic compounds.

  11. 3D-QSAR, molecular docking, and molecular dynamic simulations for prediction of new Hsp90 inhibitors based on isoxazole scaffold.

    Science.gov (United States)

    Abbasi, Maryam; Sadeghi-Aliabadi, Hojjat; Amanlou, Massoud

    2017-05-24

    Heat shock protein 90(Hsp90), as a molecular chaperone, play a crucial role in folding and proper function of many proteins. Hsp90 inhibitors containing isoxazole scaffold are currently being used in the treatment of cancer as tumor suppressers. Here in the present studies, new compounds based on isoxazole scaffold were predicted using a combination of molecular modeling techniques including three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking and molecular dynamic (MD) simulations. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were also done. The steric and electrostatic contour map of CoMFA and CoMSIA were created. Hydrophobic, hydrogen bond donor and acceptor of CoMSIA model also were generated, and new compounds were predicted by CoMFA and CoMSIA contour maps. To investigate the binding modes of the predicted compounds in the active site of Hsp90, a molecular docking simulation was carried out. MD simulations were also conducted to evaluate the obtained results on the best predicted compound and the best reported Hsp90 inhibitors in the 3D-QSAR model. Findings indicate that the predicted ligands were stable in the active site of Hsp90.

  12. Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM).

    Science.gov (United States)

    Fernandez, Michael; Caballero, Julio; Fernandez, Leyden; Sarai, Akinori

    2011-02-01

    Many articles in "in silico" drug design implemented genetic algorithm (GA) for feature selection, model optimization, conformational search, or docking studies. Some of these articles described GA applications to quantitative structure-activity relationships (QSAR) modeling in combination with regression and/or classification techniques. We reviewed the implementation of GA in drug design QSAR and specifically its performance in the optimization of robust mathematical models such as Bayesian-regularized artificial neural networks (BRANNs) and support vector machines (SVMs) on different drug design problems. Modeled data sets encompassed ADMET and solubility properties, cancer target inhibitors, acetylcholinesterase inhibitors, HIV-1 protease inhibitors, ion-channel and calcium entry blockers, and antiprotozoan compounds as well as protein classes, functional, and conformational stability data. The GA-optimized predictors were often more accurate and robust than previous published models on the same data sets and explained more than 65% of data variances in validation experiments. In addition, feature selection over large pools of molecular descriptors provided insights into the structural and atomic properties ruling ligand-target interactions.

  13. Two-dimensional quantitative structure-activity relationship study of 1,4-naphthoquinone derivatives tested against HL-60 human promyelocytic leukaemia cells.

    Science.gov (United States)

    Costa, M C A; Ferreira, M M C

    2017-04-01

    A series of 50 derivatives of 1,4-naphthoquinone tested against human HL-60 leukaemic cells showed activity at a wide range of concentrations. A multivariate quantitative structure-activity relationship (QSAR) study of 45 compounds was performed through principal component analysis (PCA) and partial least squares (PLS) regression. A good PLS regression model was obtained with two factors describing 60.1% of the total variance, and the selected descriptors were partial atomic charge at carbons 1 and 10 (C1 and C10) and total dipole moment (DIP). The calibration model exhibited the determination coefficient r2 = 0.78 and the standard error of calibration = 0.29. For external validation, r2 and the standard error of prediction were 0.74 and 0.32, respectively. DIP and C1 were the main descriptors for PCA, as well as for PLS, such that the pIC50 value increases when C1 increases and DIP diminishes. The selected descriptors are in accordance with the literature, once C10 and C1 are bound or close to the quinone oxygens involved in the production of radical anions (O2-∙). From the QSAR analysis, the structures of two new naphthoquinones were proposed and their estimated IC50 values were 1.42 and 1.13 μmol L-1.

  14. QSAR classification models for the screening of the endocrine-disrupting activity of perfluorinated compounds.

    Science.gov (United States)

    Kovarich, S; Papa, E; Li, J; Gramatica, P

    2012-01-01

    Perfluorinated compounds (PFCs) are a class of emerging pollutants still widely used in different materials as non-adhesives, waterproof fabrics, fire-fighting foams, etc. Their toxic effects include potential for endocrine-disrupting activity, but the amount of experimental data available for these pollutants is limited. The use of predictive strategies such as quantitative structure-activity relationships (QSARs) is recommended under the REACH regulation, to fill data gaps and to screen and prioritize chemicals for further experimentation, with a consequent reduction of costs and number of tested animals. In this study, local classification models for PFCs were developed to predict their T4-TTR (thyroxin-transthyretin) competing potency. The best models were selected by maximizing the sensitivity and external predictive ability. These models, characterized by robustness, good predictive power and a defined applicability domain, were applied to predict the activity of 33 other PFCs of environmental concern. Finally, classification models recently published by our research group for T4-TTR binding of brominated flame retardants and for estrogenic and anti-androgenic activity were applied to the studied perfluorinated chemicals to compare results and to further evaluate the potential for these PFCs to cause endocrine disruption.

  15. Assessing bioaccumulation of polybrominated diphenyl ethers for aquatic species by QSAR modeling.

    Science.gov (United States)

    Mansouri, Kamel; Consonni, Viviana; Durjava, Mojca Kos; Kolar, Boris; Öberg, Tomas; Todeschini, Roberto

    2012-10-01

    Polybrominated diphenyl ethers (PBDEs) are used as flame retardants in textiles, foams and plastics. Highly bioaccumulative with toxic effects including developmental neurotoxicity estrogen and thyroid hormones disruption, they are considered as persistent organic pollutants (POPs) and have been found in human tissues, wildlife and biota worldwide. But only some of them are banned from EU market. For the environmental fate studies of these compounds the bioconcentration factor (BCF) is one of the most important endpoints to start with. We applied quantitative structure-activity relationships techniques to overcome the limited experimental data and avoid more animal testing. The aim of this work was to assess the bioaccumulation of PBDEs by means of QSAR. First, a BCF dataset of specifically conducted experiments was modeled. Then the study was extended by predicting the bioaccumulation and biomagnification factors using some experimental values from the literature. Molecular descriptors were calculated using DRAGON 6. The most relevant ones were selected and resulting models were compared paying attention to the applicability domain. Copyright © 2012 Elsevier Ltd. All rights reserved.

  16. QSAR and pharmacophore modeling of natural and synthetic antimalarial prodiginines.

    Science.gov (United States)

    Singh, Baljinder; Vishwakarma, Ram A; Bharate, Sandip B

    2013-09-01

    Prodiginines are a family of linear and cyclic oligopyrrole red-pigmented compounds possessing antibacterial, anticancer and immunosuppressive activities and are produced by actinomycetes and other eubacteria. Recently, prodiginines have been reported to possess potent in vitro as well as in vivo antimalarial activity against chloroquine sensitive D6 and multi-drug resistant Dd2 strains of Plasmodium falciparum. In the present paper, a QSAR and pharmacophore modeling for a series of natural and synthetic prodiginines was performed to find out structural features which are crucial for antimalarial activity against these D6 and Dd2 Plasmodium strains. The study indicated that inertia moment 2 length, Kier Chi6 (path) index, kappa 3 index and Wiener topological index plays important role in antimalarial activity against D6 strain whereas descriptors inertia moment 2 length, ADME H-bond donors, VAMP polarization XX component and VAMP quadpole XZ component play important role in antimalarial activity against Dd2 strain. Furthermore, a five-point pharmacophore (ADHRR) model with one H-bond acceptor (A), one H-bond donor (D), one hydrophobic group (H) and two aromatic rings (R) as pharmacophore features was developed for D6 strain by PHASE module of Schrodinger suite. Similarly a six-point pharmacophore AADDRR was developed for Dd2 strain activity. All developed QSAR models showed good correlation coefficient (r² > 0.7), higher F value (F >20) and excellent predictive power (Q² > 0.6). Developed models will be highly useful for predicting antimalarial activity of new compounds and could help in designing better molecules with enhanced antimalarial activity. Furthermore, calculated ADME properties indicated drug-likeness of prodiginines.

  17. Residue-Ligand Interaction Energy (ReLIE on a Receptor-Dependent 3D-QSAR Analysis of S- and NH-DABOs as Non-Nucleoside Reverse Transcriptase Inhibitors

    Directory of Open Access Journals (Sweden)

    Monique Araújo de Brito

    2012-06-01

    Full Text Available A series of 74 dihydroalkoxybenzyloxopyrimidines (DABOs, a class of highly potent non-nucleoside reverse transcriptase inhibitors (NNRTIs, was retrieved from the literature and studied by receptor-dependent (RD three-dimensional quantitative structure-activity relationship (3D-QSAR analysis to derive RD-3D-QSAR models. The descriptors in this new method are the steric and electrostatic interaction energies of the protein-ligand complexes (per residue simulated by molecular dynamics, an approach named Residue-Ligand Interaction Energy (ReLIE. This study was performed using a training set of 59 compounds and the MKC-442/RT complex structure as reference. The ReLIE-3D-QSAR models were constructed and evaluated by genetic algorithm (GA and partial least squares (PLS. In the best equations, at least one term is related to one of the amino acid residues of the p51 subunit: Asn136, Asn137, Glu138, and Thr139. This fact implies the importance of interchain interaction (p66-p51 in the equations that best describe the structure-activity relationship for this class of compounds. The best equation shows q2 = 0.660, SEcv = 0.500, r2 = 0.930, and SEE = 0.226. The external predictive ability of this best model was evaluated using a test set of 15 compounds. In order to design more potent DABO analogues as anti-HIV/AIDS agents, substituents capable of interactions with residues like Ile94, Lys101, Tyr181, and Tyr188 should be selected. Also, given the importance of the conserved Asn136, this residue could become an attractive target for the design of novel NNRTIs with improved potency and increased ability to avoid the development of drug-resistant viruses.

  18. Antitumor activity of 3,4-ethylenedioxythiophene derivatives and quantitative structure-activity relationship analysis

    Science.gov (United States)

    Jukić, Marijana; Rastija, Vesna; Opačak-Bernardi, Teuta; Stolić, Ivana; Krstulović, Luka; Bajić, Miroslav; Glavaš-Obrovac, Ljubica

    2017-04-01

    The aim of this study was to evaluate nine newly synthesized amidine derivatives of 3,4- ethylenedioxythiophene (3,4-EDOT) for their cytotoxic activity against a panel of human cancer cell lines and to perform a quantitative structure-activity relationship (QSAR) analysis for the antitumor activity of a total of 27 3,4-ethylenedioxythiophene derivatives. Induction of apoptosis was investigated on the selected compounds, along with delivery options for the optimization of activity. The best obtained QSAR models include the following group of descriptors: BCUT, WHIM, 2D autocorrelations, 3D-MoRSE, GETAWAY descriptors, 2D frequency fingerprint and information indices. Obtained QSAR models should be relieved in elucidation of important physicochemical and structural requirements for this biological activity. Highly potent molecules have a symmetrical arrangement of substituents along the x axis, high frequency of distance between N and O atoms at topological distance 9, as well as between C and N atoms at topological distance 10, and more C atoms located at topological distances 6 and 3. Based on the conclusion given in the QSAR analysis, a new compound with possible great activity was proposed.

  19. QSARs for Plasma Protein Binding: Source Data and Predictions

    Data.gov (United States)

    U.S. Environmental Protection Agency — The dataset has all of the information used to create and evaluate 3 independent QSAR models for the fraction of a chemical unbound by plasma protein (Fub) for...

  20. Quantitative structure-activity relationship: promising advances in drug discovery platforms.

    Science.gov (United States)

    Wang, Tao; Wu, Mian-Bin; Lin, Jian-Ping; Yang, Li-Rong

    2015-12-01

    Quantitative structure-activity relationship (QSAR) modeling is one of the most popular computer-aided tools employed in medicinal chemistry for drug discovery and lead optimization. It is especially powerful in the absence of 3D structures of specific drug targets. QSAR methods have been shown to draw public attention since they were first introduced. In this review, the authors provide a brief discussion of the basic principles of QSAR, model development and model validation. They also highlight the current applications of QSAR in different fields, particularly in virtual screening, rational drug design and multi-target QSAR. Finally, in view of recent controversies, the authors detail the challenges faced by QSAR modeling and the relevant solutions. The aim of this review is to show how QSAR modeling can be applied in novel drug discovery, design and lead optimization. QSAR should intentionally be used as a powerful tool for fragment-based drug design platforms in the field of drug discovery and design. Although there have been an increasing number of experimentally determined protein structures in recent years, a great number of protein structures cannot be easily obtained (i.e., membrane transport proteins and G-protein coupled receptors). Fragment-based drug discovery, such as QSAR, could be applied further and have a significant role in dealing with these problems. Moreover, along with the development of computer software and hardware, it is believed that QSAR will be increasingly important.

  1. Comparison of fate profiles of PAHs in soil, sediments and mangrove leaves after oil spills by QSAR and QSPR.

    Science.gov (United States)

    Tansel, Berrin; Lee, Mengshan; Tansel, Derya Z

    2013-08-15

    First order removal rates for 15 polyaromatic hydrocarbons (PAHs) in soil, sediments and mangrove leaves were compared in relation to the parameters used in fate transport analyses (i.e., octanol-water partition coefficient, organic carbon-water partition coefficient, solubility, diffusivity in water, HOMO-LUMO gap, molecular size, molecular aspect ratio). The quantitative structure activity relationships (QSAR) and quantitative structure property relationships (QSPR) showed that the rate of disappearance of PAHs is correlated with their diffusivities in water as well as molecular volumes in different media. Strong correlations for the rate of disappearance of PAHs in sediments could not be obtained in relation to most of the parameters evaluated. The analyses showed that the QSAR and QSPR correlations developed for removal rates of PAHs in soils would not be adequate for sediments and plant tissues. Copyright © 2013 Elsevier Ltd. All rights reserved.

  2. Studies of the quantitative structure-activity relationship of the inhibition of xanthine oxidase by azaheterocyclic compounds

    NARCIS (Netherlands)

    Naeff, H.S.D.

    1990-01-01

    This thesis contains the results of a QSAR analysis of the interaction of bovine milk xanthine oxidase with two azaheterocyclic compounds, namely the 6-arylpteridin- 4-ones and the 8-arylhypoxanthines. Xanthine oxidase has active sites for various substrates. The studies done for this

  3. Exploring QSAR and pharmacophore mapping of structurally diverse selective matrix metalloproteinase-2 inhibitors.

    Science.gov (United States)

    Halder, Amit Kumar; Saha, Achintya; Jha, Tarun

    2013-10-01

    Matrix metalloproteinase-2 (MMP-2) is a potential target in metastases. Regression (conventional 2D QSAR) and classification (recursive partitioning (RP), Bayesian modelling) QSAR, pharmacophore mapping and 3D QSAR (comparative molecular field analysis and comparative molecular similarity analysis) were performed on 202 MMP-2 inhibitors. Quality of the regression models was justified by internal (Q(2) ) and external (R(2) Pred ) cross-validation parameters. Stepwise regression was used to develop linear model (Q(2)  = 0.822, R(2) Pred  = 0.667). Genetic algorithm developed linear (Q(2)  = 0.845, R(2) Pred  = 0.638) and spline model (Q(2)  = 0.882, R(2) Pred  = 0.644). The RP and Bayesian models showed cross-validated area under receiver operating characteristic curve (AUCROC _ CV ) of 0.805 and 0.979 respectively. QSAR models depicted importance of descriptors like five-membered rings, fractional positively charged surface area, lipophilocity and so on. Higher molecular volume was found to be detrimental. Pharmacophore mapping was performed with two tools - Hypogen and PHASE. Both models indicated that one hydrophobic and three hydrogen bond acceptor features are essential. The Pharmacophore-aligned structures were used for CoMFA (Q(2) of 0.586 and R(2) Pred of 0.689) and CoMSIA (Q(2) of 0.673 and R(2) Pred of 0.758), results of which complied with the other analyses. All modelling techniques were compared to each other. The current study may help in designing novel MMP-2 inhibitors. © 2013 Royal Pharmaceutical Society.

  4. A novel simple QSAR model for the prediction of anti-HIV activity using multiple linear regression analysis.

    Science.gov (United States)

    Afantitis, Antreas; Melagraki, Georgia; Sarimveis, Haralambos; Koutentis, Panayiotis A; Markopoulos, John; Igglessi-Markopoulou, Olga

    2006-08-01

    A quantitative-structure activity relationship was obtained by applying Multiple Linear Regression Analysis to a series of 80 1-[2-hydroxyethoxy-methyl]-6-(phenylthio) thymine (HEPT) derivatives with significant anti-HIV activity. For the selection of the best among 37 different descriptors, the Elimination Selection Stepwise Regression Method (ES-SWR) was utilized. The resulting QSAR model (R (2) (CV) = 0.8160; S (PRESS) = 0.5680) proved to be very accurate both in training and predictive stages.

  5. Quantitative structure-activity relationship of some 1-benzylbenzimidazole derivatives as antifungal agents

    Directory of Open Access Journals (Sweden)

    Podunavac-Kuzmanović Sanja O.

    2007-01-01

    Full Text Available In the present study, the antifungal activity of some 1-benzylbenzimidazole derivatives against yeast Saccharomyces cerevisiae was investigated. The tested benzimidazoles displayed in vitro antifungal activity and minimum inhibitory concentration (MIC was determined for all the compounds. Quantitative structure-activity relationship (QSAR has been used to study the relationships between the antifungal activity and lipophilicity parameter, logP, calculated by using CS Chem-Office Software version 7.0. The results are discussed on the basis of statistical data. The best QSAR model for prediction of antifungal activity of the investigated series of benzimidazoles was developed. High agreement between experimental and predicted inhibitory values was obtained. The results of this study indicate that the lipophilicity parameter has a significant effect on antifungal activity of this class of compounds, which simplify design of new biologically active molecules.

  6. Integration of QSAR models for bioconcentration suitable for REACH

    Energy Technology Data Exchange (ETDEWEB)

    Gissi, Andrea [Laboratory of Chemistry and Environmental Toxicology, IRCCS - Istituto di Ricerche Farmacologiche “Mario Negri”, via Giuseppe La Masa 19, 20156 Milan (Italy); Dipartimento di Farmacia — Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, via Orabona 4, I-70125 Bari (Italy); Nicolotti, Orazio; Carotti, Angelo; Gadaleta, Domenico [Dipartimento di Farmacia — Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, via Orabona 4, I-70125 Bari (Italy); Lombardo, Anna [Laboratory of Chemistry and Environmental Toxicology, IRCCS - Istituto di Ricerche Farmacologiche “Mario Negri”, via Giuseppe La Masa 19, 20156 Milan (Italy); Benfenati, Emilio, E-mail: benfenati@marionegri.it [Laboratory of Chemistry and Environmental Toxicology, IRCCS - Istituto di Ricerche Farmacologiche “Mario Negri”, via Giuseppe La Masa 19, 20156 Milan (Italy)

    2013-07-01

    QSAR (Quantitative Structure Activity Relationship) models can be a valuable alternative method to replace or reduce animal test required by REACH. In particular, some endpoints such as bioconcentration factor (BCF) are easier to predict and many useful models have been already developed. In this paper we describe how to integrate two popular BCF models to obtain more reliable predictions. In particular, the herein presented integrated model relies on the predictions of two among the most used BCF models (CAESAR and Meylan), together with the Applicability Domain Index (ADI) provided by the software VEGA. Using a set of simple rules, the integrated model selects the most reliable and conservative predictions and discards possible outliers. In this way, for the prediction of the 851 compounds included in the ANTARES BCF dataset, the integrated model discloses a R{sup 2} (coefficient of determination) of 0.80, a RMSE (Root Mean Square Error) of 0.61 log units and a sensitivity of 76%, with a considerable improvement in respect to the CAESAR (R{sup 2} = 0.63; RMSE = 0.84 log units; sensitivity 55%) and Meylan (R{sup 2} = 0.66; RMSE = 0.77 log units; sensitivity 65%) without discarding too many predictions (118 out of 851). Importantly, considering solely the compounds within the new integrated ADI, the R{sup 2} increased to 0.92, and the sensitivity to 85%, with a RMSE of 0.44 log units. Finally, the use of properly set safety thresholds applied for monitoring the so called “suspicious” compounds, which are those chemicals predicted in proximity of the border normally accepted to discern non-bioaccumulative from bioaccumulative substances, permitted to obtain an integrated model with sensitivity equal to 100%. - Highlights: • Applying two independent QSAR models for bioconcentration factor increases the prediction. • The concordance of the models is an important component of the integration. • The measurement of the applicability domain improves the

  7. Novel 2-benzylthio-5-(1,3,4-oxadiazol-2-yl)benzenesulfonamides with anticancer activity: Synthesis, QSAR study, and metabolic stability.

    Science.gov (United States)

    Sławiński, Jarosław; Szafrański, Krzysztof; Pogorzelska, Aneta; Żołnowska, Beata; Kawiak, Anna; Macur, Katarzyna; Belka, Mariusz; Bączek, Tomasz

    2017-05-26

    A series of novel 2-benzylthio-4-chloro-5-(5-substituted 1,3,4-oxadiazol-2-yl)benzenesulfonamides (4-27) have been synthesized as potential anticancer agents. MTT assay was carried out to determine the cytotoxic activity against three human cancer cell lines: colon cancer HCT-116, breast cancer MCF-7 and cervical cancer HeLa as well as to determine the influence on human keratinocyte cell line HaCaT. Relatively high (IC50: 7-17 μM) cytostatic activity and selectivity against HeLa cell line was found for compounds 6, 7, 9-11 and 16. While compounds 23-27 bearing styryl moieties attached to a 1,3,4-oxadiazole ring at position 5, exhibited significant activity against two and/or three cancer cell lines with IC50: 11-29 μM. Further quantitative structure-activity relationships based on molecular descriptors calculated by DRAGON software, were investigated by Orthogonal Projections to Latent Structures (OPLS) technique and Variable Influence on Projection (VIP) analysis. Considering molecular descriptors with the highest influence on projection (highest VIP values) lipophilicity of tested compounds was pointed as main factor affecting activity towards HCT-116 cell line, while structural parameters associated with presence of styryl substituent in position 5 of 1,3,4-oxadiazole ring were identified as essential for activity towards MCF-7 breast cancer. In vitro tests for metabolic stability in the presences of pooled human liver microsomes and NADPH showed that some of the most active compounds 26 and 27 presented favorable metabolic stability with t1/2 in the range of 28.1-36.0 min. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

  8. Synthesis, antimicrobial, anticancer evaluation and QSAR studies of N′-substituted benzylidene/2-hydroxynaphthalen-1-ylmethylene/3-phenylallylidene/5-oxopentylidene -4-(2-oxo-2-(4H-1,2,4-triazol-4-yl methylaminobenzohydrazides

    Directory of Open Access Journals (Sweden)

    Sumit Tahlan

    2017-05-01

    Full Text Available A series of 1,2,4-triazole derivatives (1–17 was synthesized and evaluated for its antimicrobial and anticancer potentials. Antimicrobial screening of the synthesized compounds indicated that they were most potent against Aspergillus niger and compound 14 was found to be the most active. Compound 7 showed appreciable anticancer activity against HCT 116, a colon cancer cell line. QSAR analysis indicated the importance of topological parameter, valence third order molecular connectivity index (3χv and electronic parameter, dipole moment (μ in describing the antimicrobial activity of the synthesized compounds.

  9. Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds

    Energy Technology Data Exchange (ETDEWEB)

    Alves, Vinicius M. [Laboratory of Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO 74605-220 (Brazil); Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599 (United States); Muratov, Eugene [Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599 (United States); Laboratory of Theoretical Chemistry, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa 65080 (Ukraine); Fourches, Denis [Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599 (United States); Strickland, Judy; Kleinstreuer, Nicole [ILS/Contractor Supporting the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), P.O. Box 13501, Research Triangle Park, NC 27709 (United States); Andrade, Carolina H. [Laboratory of Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO 74605-220 (Brazil); Tropsha, Alexander, E-mail: alex_tropsha@unc.edu [Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599 (United States)

    2015-04-15

    Repetitive exposure to a chemical agent can induce an immune reaction in inherently susceptible individuals that leads to skin sensitization. Although many chemicals have been reported as skin sensitizers, there have been very few rigorously validated QSAR models with defined applicability domains (AD) that were developed using a large group of chemically diverse compounds. In this study, we have aimed to compile, curate, and integrate the largest publicly available dataset related to chemically-induced skin sensitization, use this data to generate rigorously validated and QSAR models for skin sensitization, and employ these models as a virtual screening tool for identifying putative sensitizers among environmental chemicals. We followed best practices for model building and validation implemented with our predictive QSAR workflow using Random Forest modeling technique in combination with SiRMS and Dragon descriptors. The Correct Classification Rate (CCR) for QSAR models discriminating sensitizers from non-sensitizers was 71–88% when evaluated on several external validation sets, within a broad AD, with positive (for sensitizers) and negative (for non-sensitizers) predicted rates of 85% and 79% respectively. When compared to the skin sensitization module included in the OECD QSAR Toolbox as well as to the skin sensitization model in publicly available VEGA software, our models showed a significantly higher prediction accuracy for the same sets of external compounds as evaluated by Positive Predicted Rate, Negative Predicted Rate, and CCR. These models were applied to identify putative chemical hazards in the Scorecard database of possible skin or sense organ toxicants as primary candidates for experimental validation. - Highlights: • It was compiled the largest publicly-available skin sensitization dataset. • Predictive QSAR models were developed for skin sensitization. • Developed models have higher prediction accuracy than OECD QSAR Toolbox. • Putative

  10. QSAR Accelerated Discovery of Potent Ice Recrystallization Inhibitors

    Science.gov (United States)

    Briard, Jennie G.; Fernandez, Michael; de Luna, Phil; Woo, Tom. K.; Ben, Robert N.

    2016-05-01

    Ice recrystallization is the main contributor to cell damage and death during the cryopreservation of cells and tissues. Over the past five years, many small carbohydrate-based molecules were identified as ice recrystallization inhibitors and several were shown to reduce cryoinjury during the cryopreservation of red blood cells (RBCs) and hematopoietic stems cells (HSCs). Unfortunately, clear structure-activity relationships have not been identified impeding the rational design of future compounds possessing ice recrystallization inhibition (IRI) activity. A set of 124 previously synthesized compounds with known IRI activities were used to calibrate 3D-QSAR classification models using GRid INdependent Descriptors (GRIND) derived from DFT level quantum mechanical calculations. Partial least squares (PLS) model was calibrated with 70% of the data set which successfully identified 80% of the IRI active compounds with a precision of 0.8. This model exhibited good performance in screening the remaining 30% of the data set with 70% of active additives successfully recovered with a precision of ~0.7 and specificity of 0.8. The model was further applied to screen a new library of aryl-alditol molecules which were then experimentally synthesized and tested with a success rate of 82%. Presented is the first computer-aided high-throughput experimental screening for novel IRI active compounds.

  11. A Quantitative Structure Activity Relationship for acute oral toxicity of pesticides on rats: Validation, domain of application and prediction.

    Science.gov (United States)

    Hamadache, Mabrouk; Benkortbi, Othmane; Hanini, Salah; Amrane, Abdeltif; Khaouane, Latifa; Si Moussa, Cherif

    2016-02-13

    Quantitative Structure Activity Relationship (QSAR) models are expected to play an important role in the risk assessment of chemicals on humans and the environment. In this study, we developed a validated QSAR model to predict acute oral toxicity of 329 pesticides to rats because a few QSAR models have been devoted to predict the Lethal Dose 50 (LD50) of pesticides on rats. This QSAR model is based on 17 molecular descriptors, and is robust, externally predictive and characterized by a good applicability domain. The best results were obtained with a 17/9/1 Artificial Neural Network model trained with the Quasi Newton back propagation (BFGS) algorithm. The prediction accuracy for the external validation set was estimated by the Q(2)ext and the root mean square error (RMS) which are equal to 0.948 and 0.201, respectively. 98.6% of external validation set is correctly predicted and the present model proved to be superior to models previously published. Accordingly, the model developed in this study provides excellent predictions and can be used to predict the acute oral toxicity of pesticides, particularly for those that have not been tested as well as new pesticides. Copyright © 2015 Elsevier B.V. All rights reserved.

  12. SAR, QSAR and docking of anticancer flavonoids and variants: a review.

    Science.gov (United States)

    Scotti, Luciana; Bezerra Mendonça Junior, Francisco Jaime; Magalhaes Moreira, Diogo Rodrigo; da Silva, Marcelo Sobral; Pitta, Ivan R; Scotti, Marcus Tullius

    2012-01-01

    Flavonoids are phenolic compounds, secondary metabolites of plants that cause several benefits to our health, including helping the treatment against cancer. These pharmacological properties are associated with the ability of flavonoids in attenuating the generation of reactive oxygen species, acting as chelate compounds or affecting the oxi-redox cycle. In spite of the large number of information in term of SAR and QSAR, no recent review has tabulated and discussed in detail these data. In view of this, we bring here a detailed discussion of the structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) models. We have also analyzed the correlation between the chemical structure of flavonoids and analogues to their anticancer activities. A large number of methodologies have been used to identify the characteristics of these compounds with their potential anticancer: multiple linear regression, principal components analysis, comparative molecular field analysis, comparative molecular similarity indices analysis, partial least squares, neural networks, configuration of classification and regression trees, Free-Wilson, docking; using topological, structural and enthalpies' descriptors. We also discussed the use of docking models, together with QSAR models, for the virtual screening of anticancer flavonoids. The importance of docking models to the medicinal chemistry of anticancer flavonoids has increased in the last decade, especially to help in identifying the structural determinants responsible for the activity. We tabulated here the most important examples of virtual screening determined for anticancer flavonoids and we highlighted the structural determinants. The mode of action, the most potent anticancer flavonoids and hints for the structural design of anticancer flavonoids are revised in details and provided here.

  13. Quantitative Structure – Antioxidant Activity Relationships of Flavonoid Compounds

    Directory of Open Access Journals (Sweden)

    Károly Héberger

    2004-12-01

    Full Text Available A quantitative structure – antioxidant activity relationship (QSAR study of 36 flavonoids was performed using the partial least squares projection of latent structures (PLS method. The chemical structures of the flavonoids have been characterized by constitutional descriptors, two-dimensional topological and connectivity indices. Our PLS model gave a proper description and a suitable prediction of the antioxidant activities of a diverse set of flavonoids having clustering tendency.

  14. Design and prediction of new acetylcholinesterase inhibitor via quantitative structure activity relationship of huprines derivatives.

    Science.gov (United States)

    Zhang, Shuqun; Hou, Bo; Yang, Huaiyu; Zuo, Zhili

    2016-05-01

    Acetylcholinesterase (AChE) is an important enzyme in the pathogenesis of Alzheimer's disease (AD). Comparative quantitative structure-activity relationship (QSAR) analyses on some huprines inhibitors against AChE were carried out using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and hologram QSAR (HQSAR) methods. Three highly predictive QSAR models were constructed successfully based on the training set. The CoMFA, CoMSIA, and HQSAR models have values of r (2) = 0.988, q (2) = 0.757, ONC = 6; r (2) = 0.966, q (2) = 0.645, ONC = 5; and r (2) = 0.957, q (2) = 0.736, ONC = 6. The predictabilities were validated using an external test sets, and the predictive r (2) values obtained by the three models were 0.984, 0.973, and 0.783, respectively. The analysis was performed by combining the CoMFA and CoMSIA field distributions with the active sites of the AChE to further understand the vital interactions between huprines and the protease. On the basis of the QSAR study, 14 new potent molecules have been designed and six of them are predicted to be more active than the best active compound 24 described in the literature. The final QSAR models could be helpful in design and development of novel active AChE inhibitors.

  15. QSAR Modeling of COX -2 Inhibitory Activity of Some Dihydropyridine and Hydroquinoline Derivatives Using Multiple Linear Regression (MLR) Method.

    Science.gov (United States)

    Akbari, Somaye; Zebardast, Tannaz; Zarghi, Afshin; Hajimahdi, Zahra

    2017-01-01

    COX-2 inhibitory activities of some 1,4-dihydropyridine and 5-oxo-1,4,5,6,7,8-hexahydroquinoline derivatives were modeled by quantitative structure-activity relationship (QSAR) using stepwise-multiple linear regression (SW-MLR) method. The built model was robust and predictive with correlation coefficient (R 2 ) of 0.972 and 0.531 for training and test groups, respectively. The quality of the model was evaluated by leave-one-out (LOO) cross validation (LOO correlation coefficient (Q 2 ) of 0.943) and Y-randomization. We also employed a leverage approach for the defining of applicability domain of model. Based on QSAR models results, COX-2 inhibitory activity of selected data set had correlation with BEHm6 (highest eigenvalue n. 6 of Burden matrix/weighted by atomic masses), Mor03u (signal 03/unweighted) and IVDE (Mean information content on the vertex degree equality) descriptors which derived from their structures.

  16. Quantitative structure-activity relationship correlation between molecular structure and the Rayleigh enantiomeric enrichment factor.

    Science.gov (United States)

    Jammer, S; Rizkov, D; Gelman, F; Lev, O

    2015-08-01

    It was recently demonstrated that under environmentally relevant conditions the Rayleigh equation is valid to describe the enantiomeric enrichment - conversion relationship, yielding a proportional constant called the enantiomeric enrichment factor, εER. In the present study we demonstrate a quantitative structure-activity relationship model (QSAR) that describes well the dependence of εER on molecular structure. The enantiomeric enrichment factor can be predicted by the linear Hansch model, which correlates biological activity with physicochemical properties. Enantioselective hydrolysis of sixteen derivatives of 2-(phenoxy)propionate (PPMs) have been analyzed during enzymatic degradation by lipases from Pseudomonas fluorescens (PFL), Pseudomonas cepacia (PCL), and Candida rugosa (CRL). In all cases the QSAR relationships were significant with R(2) values of 0.90-0.93, and showed high predictive abilities with internal and external validations providing QLOO(2) values of 0.85-0.87 and QExt(2) values of 0.8-0.91. Moreover, it is demonstrated that this model enables differentiation between enzymes with different binding site shapes. The enantioselectivity of PFL and PCL was dictated by electronic properties, whereas the enantioselectivity of CRL was determined by lipophilicity and steric factors. The predictive ability of the QSAR model demonstrated in the present study may serve as a helpful tool in environmental studies, assisting in source tracking of unstudied chiral compounds belonging to a well-studied homologous series.

  17. Design, synthesis, antiviral activity and three-dimensional quantitative structure-activity relationship study of novel 1,4-pentadien-3-one derivatives containing the 1,3,4-oxadiazole moiety.

    Science.gov (United States)

    Gan, Xiuhai; Hu, Deyu; Li, Pei; Wu, Jian; Chen, Xuewen; Xue, Wei; Song, Baoan

    2016-03-01

    1,4-Pentadien-3-one and 1,3,4-oxadiazole derivatives possess good antiviral activities, and their substructure units are usually used in antiviral agent design. In order to discover novel molecules with high antiviral activities, a series of 1,4-pentadien-3-one derivatives containing the 1,3,4-oxadiazole moiety were designed and synthesised. Bioassays showed that most of the title compounds exhibited good inhibitory activities against tobacco mosaic virus (TMV) in vivo. The compound 8f possessing the best protective activity against TMV had an EC50 value of 135.56 mg L(-1) , which was superior to that of ribavirin (435.99 mg L(-1) ). Comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) techniques were used in three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of protective activities, with values of q(2) and r(2) for the CoMFA and CoMSIA models of 0.751 and 0.775 and 0.936 and 0.925 respectively. Compound 8k with higher protective activity (EC50 = 123.53 mg L(-1) ) according to bioassay was designed and synthesised on the basis of the 3D-QSAR models. Some of the title compounds displayed good antiviral activities. 3D-QSAR models revealed that the appropriate compact electron-withdrawing and hydrophobic group at the benzene ring could enhance antiviral activity. These results could provide important structural insights for the design of highly active 1,4-pentadien-3-one derivatives. © 2015 Society of Chemical Industry.

  18. Grid-based Continual Analysis of Molecular Interior for Drug Discovery, QSAR and QSPR.

    Science.gov (United States)

    Potemkin, Andrey V; Grishina, Maria A; Potemkin, Vladimir A

    2017-01-01

    free-orbital approach AlteQ is proposed. All the functions can be calculated using a quantum approach at a sufficient level of theory and their values can be determined in all lattice points for a molecule. Then, the molecules of a dataset can be superimposed in the lattice for the maximal coincidence (or minimal deviations) of the potentials (i) or the quantum functions (ii). The methods and criteria of the superimposition are discussed. After that a functional relationship between biological activity or property and characteristics of potentials (i) or functions (ii) is created. The methods of the quantitative relationship construction are discussed. New approaches for rational virtual drug design based on the intermolecular potentials and quantum functions are invented. All the invented methods are realized at www.chemosophia.com web page. Therefore, a set of 3D QSAR approaches for continual molecular interior study giving a lot of opportunities for virtual drug discovery, virtual screening and ligand-based drug design are invented. The continual elucidation of molecular structure is performed in the terms of intermolecular interactions potentials and in the terms of quantum functions such as electron density distribution, Laplacian and Hamiltonian of electron density distribution, potential energy distribution, the highest occupied and the lowest unoccupied molecular orbitals distribution and their superposition. To reduce time of calculations using quantum methods based on the first principles, an original quantum free-orbital approach AlteQ is proposed. The methods of the quantitative relationship construction are discussed. New approaches for rational virtual drug design based on the intermolecular potentials and quantum functions are invented. All the invented methods are realized at www.chemosophia.com web page. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  19. QSAR Study of Sucrose and Guanidine Derivatives

    African Journals Online (AJOL)

    NICO

    AHf = Eelect + Enuc – Eisol + Eatom , where Eelect is the electronic energy, Enuc is the nuclear–nuclear repulsion energy, Eisol is the energy required to strip all the valence electrons of all the atoms in the system and Eatom is the total heat of atomization of all the atoms in the system.32. The steric energy of a molecule is ...

  20. QSAR Study of Sucrose and Guanidine Derivatives

    African Journals Online (AJOL)

    NICO

    form of the solution to the quantum mechanical equation as expressed in the electronic Schrödinger ... mechanics potential energies calculated for the bonds, bond angles, dihedral angles, nonbonded atoms and so forth. It is specific to mechanics and depends upon the force-field used.33. The solvent accessibility surface ...

  1. Identification of the Structural Features of Guanine Derivatives as MGMT Inhibitors Using 3D-QSAR Modeling Combined with Molecular Docking

    Directory of Open Access Journals (Sweden)

    Guohui Sun

    2016-06-01

    Full Text Available DNA repair enzyme O6-methylguanine-DNA methyltransferase (MGMT, which plays an important role in inducing drug resistance against alkylating agents that modify the O6 position of guanine in DNA, is an attractive target for anti-tumor chemotherapy. A series of MGMT inhibitors have been synthesized over the past decades to improve the chemotherapeutic effects of O6-alkylating agents. In the present study, we performed a three-dimensional quantitative structure activity relationship (3D-QSAR study on 97 guanine derivatives as MGMT inhibitors using comparative molecular field analysis (CoMFA and comparative molecular similarity indices analysis (CoMSIA methods. Three different alignment methods (ligand-based, DFT optimization-based and docking-based alignment were employed to develop reliable 3D-QSAR models. Statistical parameters derived from the models using the above three alignment methods showed that the ligand-based CoMFA (Qcv2 = 0.672 and Rncv2 = 0.997 and CoMSIA (Qcv2 = 0.703 and Rncv2 = 0.946 models were better than the other two alignment methods-based CoMFA and CoMSIA models. The two ligand-based models were further confirmed by an external test-set validation and a Y-randomization examination. The ligand-based CoMFA model (Qext2 = 0.691, Rpred2 = 0.738 and slope k = 0.91 was observed with acceptable external test-set validation values rather than the CoMSIA model (Qext2 = 0.307, Rpred2 = 0.4 and slope k = 0.719. Docking studies were carried out to predict the binding modes of the inhibitors with MGMT. The results indicated that the obtained binding interactions were consistent with the 3D contour maps. Overall, the combined results of the 3D-QSAR and the docking obtained in this study provide an insight into the understanding of the interactions between guanine derivatives and MGMT protein, which will assist in designing novel MGMT inhibitors with desired activity.

  2. The Interplay between QSAR/QSPR Studiesand Partial Order Ranking and Formal Concept Analyses

    Directory of Open Access Journals (Sweden)

    Lars Carlsen

    2009-04-01

    Full Text Available The often observed scarcity of physical-chemical and well as toxicological data hampers the assessment of potentially hazardous chemicals released to the environment. In such cases Quantitative Structure-Activity Relationships/Quantitative Structure-Property Relationships (QSAR/QSPR constitute an obvious alternative for rapidly, effectively and inexpensively generatng missing experimental values. However, typically further treatment of the data appears necessary, e.g., to elucidate the possible relations between the single compounds as well as implications and associations between the various parameters used for the combined characterization of the compounds under investigation. In the present paper the application of QSAR/QSPR in combination with Partial Order Ranking (POR methodologies will be reviewed and new aspects using Formal Concept Analysis (FCA will be introduced. Where POR constitutes an attractive method for, e.g., prioritizing a series of chemical substances based on a simultaneous inclusion of a range of parameters, FCA gives important information on the implications associations between the parameters. The combined approach thus constitutes an attractive method to a preliminary assessment of the impact on environmental and human health by primary pollutants or possibly by a primary pollutant well as a possible suite of transformation subsequent products that may be both persistent in and bioaccumulating and toxic.The present review focus on the environmental – and human health impact by residuals of the rocket fuel 1,1-dimethyl- hydrazine (heptyl and its transformation products as an illustrative example.

  3. Integrated machine learning, molecular docking and 3D-QSAR based approach for identification of potential inhibitors of trypanosomal N-myristoyltransferase.

    Science.gov (United States)

    Singh, Nidhi; Shah, Priyanka; Dwivedi, Hemlata; Mishra, Shikha; Tripathi, Renu; Sahasrabuddhe, Amogh A; Siddiqi, Mohammad Imran

    2016-11-15

    N-Myristoyltransferase (NMT) catalyzes the transfer of myristate to the amino-terminal glycine of a subset of proteins, a co-translational modification involved in trafficking substrate proteins to membrane locations, stabilization and protein-protein interactions. It is a studied and validated pre-clinical drug target for fungal and parasitic infections. In the present study, a machine learning approach, docking studies and CoMFA analysis have been integrated with the objective of translation of knowledge into a pipelined workflow towards the identification of putative hits through the screening of large compound libraries. In the proposed pipeline, the reported parasitic NMT inhibitors have been used to develop predictive machine learning classification models. Simultaneously, a TbNMT complex model was generated to establish the relationship between the binding mode of the inhibitors for LmNMT and TbNMT through molecular dynamics simulation studies. A 3D-QSAR model was developed and used to predict the activity of the proposed hits in the subsequent step. The hits classified as active based on the machine learning model were assessed as the potential anti-trypanosomal NMT inhibitors through molecular docking studies, predicted activity using a QSAR model and visual inspection. In the final step, the proposed pipeline was validated through in vitro experiments. A total of seven hits have been proposed and tested in vitro for evaluation of dual inhibitory activity against Leishmania donovani and Trypanosoma brucei. Out of these five compounds showed significant inhibition against both of the organisms. The common topmost active compound SEW04173 belongs to a pyrazole carboxylate scaffold and is anticipated to enrich the chemical space with enhanced potency through optimization.

  4. On the development and validation of QSAR models.

    Science.gov (United States)

    Gramatica, Paola

    2013-01-01

    The fundamental and more critical steps that are necessary for the development and validation of QSAR models are presented in this chapter as best practices in the field. These procedures are discussed in the context of predictive QSAR modelling that is focused on achieving models of the highest statistical quality and with external predictive power. The most important and most used statistical parameters needed to verify the real performances of QSAR models (of both linear regression and classification) are presented. Special emphasis is placed on the validation of models, both internally and externally, as well as on the need to define model applicability domains, which should be done when models are employed for the prediction of new external compounds.

  5. A Quantitative Structure-Activity Relationship and Molecular Modeling Study on a Series of Heteroaryl- and Heterocyclyl-Substituted Imidazo[1,2-a]Pyridine Derivatives Acting as Acid Pump Antagonists

    Directory of Open Access Journals (Sweden)

    Neeraj Agarwal

    2013-01-01

    Full Text Available A quantitative structure-activity relationship (QSAR and molecular docking study has been performed on a series of heteroaryl- and heterocyclyl-substituted imidazo[1,2-a]pyridine derivatives acting as acid pump antagonists in order to have a better understanding of the mechanism of H+/K+-ATPase inhibition. The QSAR study shows a significant correlation of activity with Global Topological Charge Indices (GTCI of the compounds and the hydrophobic constant of some substituents, indicating that the charge transfer within the molecule and the hydrophobic property of some substituents will be the controlling factor of the activity of these compounds and that there can be dispersion interaction between the molecules and the receptor, where some substituents may have hydrophobic interaction, too. Based on this correlation some new compounds with higher potency have been predicted and their docking study has been performed to see if they can have better interaction with the receptor. The ADME properties of these predicted compounds have also been reported that follow Lipinski’s rule of five.

  6. Evaluation of Novel Dual Acetyl- and Butyrylcholinesterase Inhibitors as Potential Anti-Alzheimer’s Disease Agents Using Pharmacophore, 3D-QSAR, and Molecular Docking Approaches

    Directory of Open Access Journals (Sweden)

    Xiaocong Pang

    2017-07-01

    Full Text Available DL0410, containing biphenyl and piperidine skeletons, was identified as an acetylcholinesterase (AChE and butyrylcholinesterase (BuChE inhibitor through high-throughput screening assays, and further studies affirmed its efficacy and safety for Alzheimer’s disease treatment. In our study, a series of novel DL0410 derivatives were evaluated for inhibitory activities towards AChE and BuChE. Among these derivatives, compounds 6-1 and 7-6 showed stronger AChE and BuChE inhibitory activities than DL0410. Then, pharmacophore modeling and three-dimensional quantitative structure activity relationship (3D-QSAR models were performed. The R2 of AChE and BuChE 3D-QSAR models for training set were found to be 0.925 and 0.883, while that of the test set were 0.850 and 0.881, respectively. Next, molecular docking methods were utilized to explore the putative binding modes. Compounds 6-1 and 7-6 could interact with the amino acid residues in the catalytic anionic site (CAS and peripheral anionic site (PAS of AChE/BuChE, which was similar with DL0410. Kinetics studies also suggested that the three compounds were all mixed-types of inhibitors. In addition, compound 6-1 showed better absorption and blood brain barrier permeability. These studies provide better insight into the inhibitory behaviors of DL0410 derivatives, which is beneficial for rational design of AChE and BuChE inhibitors in the future.

  7. QSAR screening of 70,983 REACH substances for genotoxic carcinogenicity, mutagenicity and developmental toxicity in the ChemScreen project

    DEFF Research Database (Denmark)

    Wedebye, Eva Bay; Dybdahl, Marianne; Nikolov, Nikolai Georgiev

    2015-01-01

    for information requirements. As no testing for reproductive effects should be performed in REACH on known genotoxic carcinogens or germ cell mutagens with appropriate risk management measures implemented, a QSAR pre-screen for 70,983 REACH substances was performed. Sixteen models and three decision algorithms...... were used to reach overall predictions of substances with potential effects with the following result: 6.5% genotoxic carcinogens, 16.3% mutagens, 11.5% developmental toxicants. These results are similar to findings in earlier QSAR and experimental studies of chemical inventories, and illustrate how...... QSAR predictions may be used to identify potential genotoxic carcinogens, mutagens and developmental toxicants by high-throughput virtual screening....

  8. The importance of molecular structures, endpoints' values, and predictivity parameters in QSAR research: QSAR analysis of a series of estrogen receptor binders.

    Science.gov (United States)

    Li, Jiazhong; Gramatica, Paola

    2010-11-01

    Quantitative structure-activity relationship (QSAR) methodology aims to explore the relationship between molecular structures and experimental endpoints, producing a model for the prediction of new data; the predictive performance of the model must be checked by external validation. Clearly, the qualities of chemical structure information and experimental endpoints, as well as the statistical parameters used to verify the external predictivity have a strong influence on QSAR model reliability. Here, we emphasize the importance of these three aspects by analyzing our models on estrogen receptor binders (Endocrine disruptor knowledge base (EDKB) database). Endocrine disrupting chemicals, which mimic or antagonize the endogenous hormones such as estrogens, are a hot topic in environmental and toxicological sciences. QSAR shows great values in predicting the estrogenic activity and exploring the interactions between the estrogen receptor and ligands. We have verified our previously published model for additional external validation on new EDKB chemicals. Having found some errors in the used 3D molecular conformations, we redevelop a new model using the same data set with corrected structures, the same method (ordinary least-square regression, OLS) and DRAGON descriptors. The new model, based on some different descriptors, is more predictive on external prediction sets. Three different formulas to calculate correlation coefficient for the external prediction set (Q2 EXT) were compared, and the results indicated that the new proposal of Consonni et al. had more reasonable results, consistent with the conclusions from regression line, Williams plot and root mean square error (RMSE) values. Finally, the importance of reliable endpoints values has been highlighted by comparing the classification assignments of EDKB with those of another estrogen receptor binders database (METI): we found that 16.1% assignments of the common compounds were opposite (20 among 124 common

  9. Structure–Biological Function Relationship Extended to Mitotic Arrest-Deficient 2-Like Protein Mad2 Native and Mutants-New Opportunity for Genetic Disorder Control

    Science.gov (United States)

    Avram, Speranta; Milac, Adina; Mernea, Maria; Mihailescu, Dan; Putz, Mihai V.; Buiu, Catalin

    2014-01-01

    Overexpression of mitotic arrest-deficient proteins Mad1 and Mad2, two components of spindle assembly checkpoint, is a risk factor for chromosomal instability (CIN) and a trigger of many genetic disorders. Mad2 transition from inactive open (O-Mad2) to active closed (C-Mad2) conformations or Mad2 binding to specific partners (cell-division cycle protein 20 (Cdc20) or Mad1) were targets of previous pharmacogenomics studies. Here, Mad2 binding to Cdc20 and the interconversion rate from open to closed Mad2 were predicted and the molecular features with a critical contribution to these processes were determined by extending the quantitative structure-activity relationship (QSAR) method to large-size proteins such as Mad2. QSAR models were built based on available published data on 23 Mad2 mutants inducing CIN-related functional changes. The most relevant descriptors identified for predicting Mad2 native and mutants action mechanism and their involvement in genetic disorders are the steric (van der Waals area and solvent accessible area and their subdivided) and energetic van der Waals energy descriptors. The reliability of our QSAR models is indicated by significant values of statistical coefficients: Cross-validated correlation q2 (0.53–0.65) and fitted correlation r2 (0.82–0.90). Moreover, based on established QSAR equations, we rationally design and analyze nine de novo Mad2 mutants as possible promoters of CIN. PMID:25411801

  10. Structure-biological function relationship extended to mitotic arrest-deficient 2-like protein Mad2 native and mutants-new opportunity for genetic disorder control.

    Science.gov (United States)

    Avram, Speranta; Milac, Adina; Mernea, Maria; Mihailescu, Dan; Putz, Mihai V; Buiu, Catalin

    2014-11-18

    Overexpression of mitotic arrest-deficient proteins Mad1 and Mad2, two components of spindle assembly checkpoint, is a risk factor for chromosomal instability (CIN) and a trigger of many genetic disorders. Mad2 transition from inactive open (O-Mad2) to active closed (C-Mad2) conformations or Mad2 binding to specific partners (cell-division cycle protein 20 (Cdc20) or Mad1) were targets of previous pharmacogenomics studies. Here, Mad2 binding to Cdc20 and the interconversion rate from open to closed Mad2 were predicted and the molecular features with a critical contribution to these processes were determined by extending the quantitative structure-activity relationship (QSAR) method to large-size proteins such as Mad2. QSAR models were built based on available published data on 23 Mad2 mutants inducing CIN-related functional changes. The most relevant descriptors identified for predicting Mad2 native and mutants action mechanism and their involvement in genetic disorders are the steric (van der Waals area and solvent accessible area and their subdivided) and energetic van der Waals energy descriptors. The reliability of our QSAR models is indicated by significant values of statistical coefficients: Cross-validated correlation q2 (0.53-0.65) and fitted correlation r2 (0.82-0.90). Moreover, based on established QSAR equations, we rationally design and analyze nine de novo Mad2 mutants as possible promoters of CIN.

  11. Structure–Biological Function Relationship Extended to Mitotic Arrest-Deficient 2-Like Protein Mad2 Native and Mutants-New Opportunity for Genetic Disorder Control

    Directory of Open Access Journals (Sweden)

    Speranta Avram

    2014-11-01

    Full Text Available Overexpression of mitotic arrest-deficient proteins Mad1 and Mad2, two components of spindle assembly checkpoint, is a risk factor for chromosomal instability (CIN and a trigger of many genetic disorders. Mad2 transition from inactive open (O-Mad2 to active closed (C-Mad2 conformations or Mad2 binding to specific partners (cell-division cycle protein 20 (Cdc20 or Mad1 were targets of previous pharmacogenomics studies. Here, Mad2 binding to Cdc20 and the interconversion rate from open to closed Mad2 were predicted and the molecular features with a critical contribution to these processes were determined by extending the quantitative structure-activity relationship (QSAR method to large-size proteins such as Mad2. QSAR models were built based on available published data on 23 Mad2 mutants inducing CIN-related functional changes. The most relevant descriptors identified for predicting Mad2 native and mutants action mechanism and their involvement in genetic disorders are the steric (van der Waals area and solvent accessible area and their subdivided and energetic van der Waals energy descriptors. The reliability of our QSAR models is indicated by significant values of statistical coefficients: Cross-validated correlation q2 (0.53–0.65 and fitted correlation r2 (0.82–0.90. Moreover, based on established QSAR equations, we rationally design and analyze nine de novo Mad2 mutants as possible promoters of CIN.

  12. QSARs for phenols and phenolates: oxidation potential as a predictor of reaction rate constants with photochemically produced oxidants.

    Science.gov (United States)

    Arnold, William A; Oueis, Yan; O'Connor, Meghan; Rinaman, Johanna E; Taggart, Miranda G; McCarthy, Rachel E; Foster, Kimberley A; Latch, Douglas E

    2017-03-22

    Quantitative structure-activity relationships (QSARs) for prediction of the reaction rate constants of phenols and phenolates with three photochemically produced oxidants, singlet oxygen, carbonate radical, and triplet excited state sensitizers/organic matter, are developed. The predictive variable is the one-electron oxidation potential (E 1 ), which is calculated for each species using density functional theory. The reaction rate constants are obtained from the literature, and for singlet oxygen, are augmented with new experimental data. Calculated E 1 values have a mean unsigned error compared to literature values of 0.04-0.06 V. For singlet oxygen, a single linear QSAR that includes both phenols and phenolates is developed that predicts experimental rate constants, on average, to within a factor of three. Predictions for only 6 out of 87 compounds are off by more than a factor of 10. A more limited data set for carbonate radical reactions with phenols and phenolates also gives a single linear QSAR with prediction of rate constant being accurate to within a factor of three. The data for the reactions of phenols with triplet state sensitizers demonstrate that two sensitizers, 2-acetonaphthone and methylene blue, most closely predict the reactivity trend of triplet excited state organic matter with phenols. Using sensitizers with stronger reduction potentials could lead to overestimation of rate constants and thus underestimation of phenolic pollutant persistence.

  13. Optimization of antiproliferative activity of substituted phenyl 4-(2-oxoimidazolidin-1-yl) benzenesulfonates: QSAR and CoMFA analyses.

    Science.gov (United States)

    Masand, Vijay H; Mahajan, Devidas T; Alafeefy, Ahmed M; Bukhari, Syed Nasir Abbas; Elsayed, Nahed N

    2015-09-18

    Multiple separate quantitative structure-activity relationships (QSARs) models were built for the antiproliferative activity of substituted Phenyl 4-(2-Oxoimidazolidin-1-yl)-benzenesulfonates (PIB-SOs). A variety of descriptors were considered for PIB-SOs through QSAR model building. Genetic algorithm (GA), available in QSARINS, was employed to select optimum number and set of descriptors to build the multi-linear regression equations for a dataset of PIB-SOs. The best three parametric models were subjected to thorough internal and external validation along with Y-randomization using QSARINS, according to the OECD principles for QSAR model validation. The models were found to be statistically robust with high external predictivity. The best three parametric model, based on steric, 3D- and finger print descriptors, was found to have R(2)=0.91, R(2)ex=0.89, and CCCex=0.94. The CoMFA model, which is based on a combination of steric and electrostatic effects and graphically inferred using contour plots, gave F=229.34, R(2)CV=0.71 and R(2)=0.94. Steric repulsion, frequency of occurrence of carbon and nitrogen at topological distance of seven, and internal electronic environment of the molecule were found to have correlation with the anti-tumor activity of PIB-SOs. Copyright © 2015 Elsevier B.V. All rights reserved.

  14. Study The Relationship Between Intercellular Adhesion Molecules ...

    African Journals Online (AJOL)

    plasma concentration of ICAM-1 as a marker for endothelial activation among type 2 diabetic patients with or without nephropathy (as tool in early diagnosis of nephropathy as major diabetic complications) also to explore the relationship between plasma level of ICAM-1 and insulin resistance in the studied patients.

  15. Cytotoxic lanostane-type triterpenoids from the fruiting bodies of Ganoderma lucidum and their structure?activity relationships

    OpenAIRE

    Chen, Shaodan; Li, Xiangmin; Yong, Tianqiao; Wang, Zhanggen; Su, Jiyan; Jiao, Chunwei; Xie, Yizhen; Yang, Burton B.

    2016-01-01

    We conducted a study of Ganoderma lucidum metabolites and isolated 35 lanostane-type triterpenoids, including 5 new ganoderols (1-5). By spectroscopy, we compared the structures of these compounds with known related compounds in this group. All of the isolated compounds were assayed for their effect against the human breast carcinoma cell line MDA-MB-231 and hepatocellular carcinoma cell line HepG2. Corresponding three-dimensional quantitative structure?activity relationship (3D-QSAR) models ...

  16. Predicting human intestinal absorption of diverse chemicals using ensemble learning based QSAR modeling approaches.

    Science.gov (United States)

    Basant, Nikita; Gupta, Shikha; Singh, Kunwar P

    2016-04-01

    Human intestinal absorption (HIA) of the drugs administered through the oral route constitutes an important criterion for the candidate molecules. The computational approach for predicting the HIA of molecules may potentiate the screening of new drugs. In this study, ensemble learning (EL) based qualitative and quantitative structure-activity relationship (SAR) models (gradient boosted tree, GBT and bagged decision tree, BDT) have been established for the binary classification and HIA prediction of the chemicals, using the selected molecular descriptors. The structural diversity of the chemicals and the nonlinear structure in the considered data were tested by the similarity index and Brock-Dechert-Scheinkman statistics. The external predictive power of the developed SAR models was evaluated through the internal and external validation procedures recommended in the literature. All the statistical criteria parameters derived for the performance of the constructed SAR models were above their respective thresholds suggesting for their robustness for future applications. In complete data, the qualitative SAR models rendered classification accuracy of >99%, while the quantitative SAR models yielded correlation (R(2)) of >0.91 between the measured and predicted HIA values. The performances of the EL-based SAR models were also compared with the linear models (linear discriminant analysis, LDA and multiple linear regression, MLR). The GBT and BDT SAR models performed better than the LDA and MLR methods. A comparison of our models with the previously reported QSARs for HIA prediction suggested for their better performance. The results suggest for the appropriateness of the developed SAR models to reliably predict the HIA of structurally diverse chemicals and can serve as useful tools for the initial screening of the molecules in the drug development process. Copyright © 2016 Elsevier Ltd. All rights reserved.

  17. Quantitative structure-activity relationship study of betulinic acid derivatives against HIV using SMILES-based descriptors.

    Science.gov (United States)

    Worachartcheewan, Apilak; Toropova, Alla P; Toropov, Andrey A; Siriwong, Suphakit; Prapojanasomboon, Jatupat; Prachayasittikul, Virapong; Nanatasenamat, Chanin

    2018-01-11

    Human immunodeficiency virus (HIV) is the causative agent of acquired immunodeficiency syndrome (AIDS) that imposes a global health burden. Therefore, HIV therapeutic agents have been discovery and development. To construct quantitative-structure activity relationship (QSAR) models of betulinic acid derivatives with anti-HIV activity using simplified molecular-input line-entry system (SMILES)-based descriptors Methods: A data set of 107 betulinic acid derivatives and their anti-HIV activity was used to develop QSAR models. The SMILES format of the compounds was employed as descriptors for model construction using the CORAL software by means of the Monte Carlo method. Constructed QSAR models provided good correlation coefficients (R2) and root mean squared error (RMSE) with values in the range of 0.5660-0.5890 and 0.963-1.020, respectively, for the training set, value of 0.7206-0.7837 and RMSE as 0.609-1.250, respectively, for the calibration set, and values of 0.6257-0.7748 and RMSE as 0.837-0.995, respectively, for the validation set. The best QSAR model displayed statistical parameters for training set: R2 = 0.5660 and RMSE = 0.963; calibration set: R2 = 0.7273and RMSE = 0.609, and validation set: R2 = 0.7748 and RMSE = 0.972. In addition, features of the molecular structure that are promoters of the endpoint increase and decrease were defined and discussed. These are the basis for the mechanistic interpretation of the suggested models. These findings provide useful knowledge for guiding the design of novel compounds with promising anti-HIV activity. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  18. MODEL QSAR SENYAWA FLUOROKUINOLON BARU SEBAGAI ZAT ANTIBAKTERI Salmonella thypimurium

    Directory of Open Access Journals (Sweden)

    Eva Vaulina

    2006-11-01

    Full Text Available Modelling of novel Fluoroquinolone derivates as antibacterial compund of Salmonella thypimurium was conducted. The research was done as an initial step in discovering some new Fluoroquinolone compounds which have higher activity to Salmonella thypimurium. There are 16 compunds that use as the material of the research and they already have antibacterial activity data that expressed in Minimal Inhibitory Concentration (MIC, mg/mL. Calculation was performed by semiempirical AM1 method. The QSAR model was determined by multilinear regression analysis, with Log MIC as dependent variable and the independent variables are atomic net charges of C5 (qC5 and C7 (qC7, dipole moment (m, polarizability (a, n-octanol-water coefficien partition (Log P, molecular weight (Mw, and surface area of van der Waals (AvdW. The relationship between Log MIC and the descriptors which performed by statistical analysis is:(Log MIC = -2.119 + 34.541(qC5 – 19.748(qC7 – 0.919polar + 1.170logP + 0.111(Mw – 0.003(Avdw, with n =16, r = 0.907, r2 = 0.822, SD = 0.288, F calc = 6.938, F table = 3.374 , F calc/F table = 2.056 and PRESS = 0.749. The research can obtain the new coumpounds that modified from compound number 16 (etil fluoroquinolone, MIC prediction = 0.0354 mg/mL, (etil fluoroquinlone fosfate, 2.84. 10-19mg/mL, and (isopropyl fluoroquinlone, 0.1085 mg/mL, and compound number 2 (m-nitro fluoroquinolone sulfonat, 1.32. 10-11mg/mL. This results can be suggested to synthesis step

  19. Quantitative Structure-Activity Relationship Analysis of Xanthone Derivates as Cytotoxic Agents in Liver Cancer Cell Line HepG2

    OpenAIRE

    Isnatin Miladiyah; Iqmal Tahir; Jumina Jumina; Sofia Mubarika; Mustofa Mustofa

    2016-01-01

    The study of xanthone derivatives as cytotoxic agents in cancer is increasing. This study was conducted to explore the Quantitative Structure-Activity Relationship (QSAR) of xanthones as cytotoxic agents in HepG2 cells, to find compounds with better potency. The data set were taken from the previous study, involving 26 xanthone derivates and their cytotoxic activities in Inhibitory Concentration 50% (IC50). The parameters (descriptors) were obtained from quantum mechanics calculation using se...

  20. A Comparative Study on Selective PPAR Modulators Through Quantitative Structure-Activity Relationship, Pharmacophore and Docking Analyses.

    Science.gov (United States)

    Nandy, Ashis; Roy, Kunal; Saha, Achintya

    2017-06-08

    Metabolic syndrome is a matrix of different metabolic disorders which are the leading cause of death in human beings. Peroxysome proliferated activated receptor (PPAR) is a nuclear receptor involvedin metabolism of fats and glucose. In order to explore structural requirements for selective PPAR modulators to control lipid and carbohydrate metabolism, the multi-cheminformatics studies have been performed. Insilico modeling studies have been performed on a diverse set of PPAR modulators through quantitative structural-activity relationship (QSAR), pharmacophore mapping and docking studies. It is observed that the presence of an amide fragment (-CONHRPh) has a detrimental effect while an aliphatic ether linkage has a beneficial effect on PPARα modulation. On the other hand, the presence of an amide fragment has a positive effect on PPARδ modulation, but the aliphatic ether linkage and substituted aromatic ring in the molecular scaffold are very much essential for imparting potent and selective PPARγ modulation. Negative ionizable features (i.e. polar fragments) must be present in PPARδ and  modulators, but a hydrophobic feature is the prime requirement for PPARγ modulation. Here, the essential structural features have been explored for selective modulation of each subtype of PPAR in order to design new modulators with improved activity/selectivity. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  1. A quantitative structure–activity relationship study on HIV-1 integrase inhibitors using genetic algorithm, artificial neural networks and different statistical methods

    Directory of Open Access Journals (Sweden)

    Ghasem Ghasemi

    2016-09-01

    Full Text Available In this work, quantitative structure–activity relationship (QSAR study has been done on tricyclic phthalimide analogues acting as HIV-1 integrase inhibitors. Forty compounds were used in this study. Genetic algorithm (GA, artificial neural network (ANN and multiple linear regressions (MLR were utilized to construct the non-linear and linear QSAR models. It revealed that the GA–ANN model was much better than other models. For this purpose, ab initio geometry optimization performed at B3LYP level with a known basis set 6–31G (d. Hyperchem, ChemOffice and Gaussian 98W softwares were used for geometry optimization of the molecules and calculation of the quantum chemical descriptors. To include some of the correlation energy, the calculation was done with the density functional theory (DFT with the same basis set and Becke’s three parameter hybrid functional using the LYP correlation functional (B3LYP/6–31G (d. For the calculations in solution phase, the polarized continuum model (PCM was used and also included optimizations at gas-phase B3LYP/6–31G (d level for comparison. In the aqueous phase, the root–mean–square errors of the training set and the test set for GA–ANN model using jack–knife method, were 0.1409, 0.1804, respectively. In the gas phase, the root–mean–square errors of the training set and the test set for GA–ANN model were 0.1408, 0.3103, respectively. Also, the R2 values in the aqueous and the gas phase were obtained as 0.91, 0.82, respectively.

  2. Evaluation of OASIS QSAR Models Using ToxCast™ in Vitro Estrogen and Androgen Receptor Binding Data and Application in an Integrated Endocrine Screening Approach.

    Science.gov (United States)

    Bhhatarai, Barun; Wilson, Daniel M; Price, Paul S; Marty, Sue; Parks, Amanda K; Carney, Edward

    2016-09-01

    Integrative testing strategies (ITSs) for potential endocrine activity can use tiered in silico and in vitro models. Each component of an ITS should be thoroughly assessed. We used the data from three in vitro ToxCast™ binding assays to assess OASIS, a quantitative structure-activity relationship (QSAR) platform covering both estrogen receptor (ER) and androgen receptor (AR) binding. For stronger binders (described here as AC50 75%) and specificity (> 86%) for ER as well as both high sensitivity (92-100%) and specificity (70-81%) for AR. For compounds within the domains of the ER and AR QSAR models that bound with AC50 approach wherein a) QSAR is used to identify compounds in-domain of the ER or AR binding models and predicted to bind; b) those compounds are screened in vitro to assess binding potency; and c) the stronger binders (AC50 vitro, that require metabolism to manifest activity, or for which in vivo AR testing is in order, need to be assessed differently. Bhhatarai B, Wilson DM, Price PS, Marty S, Parks AK, Carney E. 2016. Evaluation of OASIS QSAR models using ToxCast™ in vitro estrogen and androgen receptor binding data and application in an integrated endocrine screening approach. Environ Health Perspect 124:1453-1461; http://dx.doi.org/10.1289/EHP184.

  3. An automated tool for obtaining QSAR-ready series of compounds using semantic web technologies.

    Science.gov (United States)

    López-Massaguer, Oriol; Sanz, Ferran; Pastor, Manuel

    2017-09-07

    We describe an application (Collector) for obtaining series of compounds annotated with bioactivity data, ready to be used for the development of quantitative structure-activity relationships (QSAR) models. The tool extracts data from the 'Open Pharmacological Space' (OPS) developed by the Open PHACTS project, using as input a valid name of the biological target. Collector uses the OPS ontologies for expanding the query using all known target synonyms and extracts compounds with bioactivity data against the target from multiple sources. The extracted data can be filtered to retain only drug-like compounds and the bioactivities can be automatically summarised to assign a single value per compound, yielding data ready to be used for QSAR modeling. The data obtained is locally stored facilitating the traceability and auditability of the process. Collector was used successfully for the development of models for toxicity endpoints within the eTOX project. The software is available at http://phi.upf.edu/collector . The source code is located at https://github.com/phi-grib/Collector and is free for use under the GPL3 license. The web version is hosted at http://collector.upf.edu /. Supplementary data are available at Bioinformatics online.

  4. Development of QSARs for parameterizing Physiology Based ToxicoKinetic models.

    Science.gov (United States)

    Sarigiannis, Dimosthenis Α; Papadaki, Krystalia; Kontoroupis, Periklis; Karakitsios, Spyros P

    2017-08-01

    A Quantitative Structure Activity Relationship (QSAR) model was developed in order to predict physicochemical and biochemical properties of industrial chemicals of various groups. This model was based on the solvation equation, originally proposed by Abraham. In this work Abraham's solvation model got parameterized using artificial intelligence techniques such as artificial neural networks (ANNs) for the prediction of partitioning into kidney, heart, adipose, liver, muscle, brain and lung for the estimation of the bodyweight-normalized maximal metabolic velocity (Vmax) and the Michaelis - Menten constant (Km). Model parameterization using ANNs was compared to the use of non-linear regression (NLR) for organic chemicals. The coupling of ANNs with Abraham's solvation equation resulted in a model with strong predictive power (R2 up to 0.95) for both partitioning and biokinetic parameters. The proposed model outperformed other QSAR models found in the literature, especially with regard to the estimation and prediction of key biokinetic parameters such as Km. The results show that the physicochemical descriptors used in the model successfully describe the complex interactions of the micro-processes governing chemical distribution and metabolism in human tissues. Moreover, ANNs provide a flexible mathematical framework to capture the non-linear biochemical and biological interactions compared to less flexible regression techniques. Copyright © 2017. Published by Elsevier Ltd.

  5. Development of a Sigma-2 Receptor affinity filter through a Monte Carlo based QSAR analysis.

    Science.gov (United States)

    Rescifina, Antonio; Floresta, Giuseppe; Marrazzo, Agostino; Parenti, Carmela; Prezzavento, Orazio; Nastasi, Giovanni; Dichiara, Maria; Amata, Emanuele

    2017-08-30

    For the first time in sigma-2 (σ2) receptor field, a quantitative structure-activity relationship (QSAR) model has been built using pKi values of the whole set of known selective σ2 receptor ligands (548 compounds), taken from the Sigma-2 Receptor Selective Ligands Database (S2RSLDB) (http://www.researchdsf.unict.it/S2RSLDB/), through the Monte Carlo technique and employing the software CORAL. The model has been developed by using a large and structurally diverse set of compounds, allowing for a prediction of different populations of chemical compounds endpoint (σ2 receptor pKi). The statistical quality reached, suggested that model for pKi determination is robust and possesses a satisfactory predictive potential. The statistical quality is high for both visible and invisible sets. The screening of the FDA approved drugs, external to our dataset, suggested that sixteen compounds might be repositioned as σ2 receptor ligands (predicted pKi≥8). A literature check showed that six of these compounds have already been tested for affinity at σ2 receptor and, of these, two (Flunarizine and Terbinafine) have shown an experimental σ2 receptor pKi>7. This suggests that this QSAR model may be used as focusing screening filter in order to prospectively find or repurpose new drugs with high affinity for the σ2 receptor, and overall allowing for an enhanced hit rate respect to a random screening. Copyright © 2017 Elsevier B.V. All rights reserved.

  6. The use of pseudo-equilibrium constant affords improved QSAR models of human plasma protein binding.

    Science.gov (United States)

    Zhu, Xiang-Wei; Sedykh, Alexander; Zhu, Hao; Liu, Shu-Shen; Tropsha, Alexander

    2013-07-01

    To develop accurate in silico predictors of Plasma Protein Binding (PPB). Experimental PPB data were compiled for over 1,200 compounds. Two endpoints have been considered: (1) fraction bound (%PPB); and (2) the logarithm of a pseudo binding constant (lnKa) derived from %PPB. The latter metric was employed because it reflects the PPB thermodynamics and the distribution of the transformed data is closer to normal. Quantitative Structure-Activity Relationship (QSAR) models were built with Dragon descriptors and three statistical methods. Five-fold external validation procedure resulted in models with the prediction accuracy (R²) of 0.67 ± 0.04 and 0.66 ± 0.04, respectively, and the mean absolute error (MAE) of 15.3 ± 0.2% and 13.6 ± 0.2%, respectively. Models were validated with two external datasets: 173 compounds from DrugBank, and 236 chemicals from the US EPA ToxCast project. Models built with lnKa were significantly more accurate (MAE of 6.2-10.7 %) than those built with %PPB (MAE of 11.9-17.6 %) for highly bound compounds both for the training and the external sets. The pseudo binding constant (lnKa) is more appropriate for characterizing PPB binding than conventional %PPB. Validated QSAR models developed herein can be applied as reliable tools in early drug development and in chemical risk assessment.

  7. A cascaded QSAR model for efficient prediction of overall power conversion efficiency of all-organic dye-sensitized solar cells.

    Science.gov (United States)

    Li, Hongzhi; Zhong, Ziyan; Li, Lin; Gao, Rui; Cui, Jingxia; Gao, Ting; Hu, Li Hong; Lu, Yinghua; Su, Zhong-Min; Li, Hui

    2015-05-30

    A cascaded model is proposed to establish the quantitative structure-activity relationship (QSAR) between the overall power conversion efficiency (PCE) and quantum chemical molecular descriptors of all-organic dye sensitizers. The cascaded model is a two-level network in which the outputs of the first level (JSC, VOC, and FF) are the inputs of the second level, and the ultimate end-point is the overall PCE of dye-sensitized solar cells (DSSCs). The model combines quantum chemical methods and machine learning methods, further including quantum chemical calculations, data division, feature selection, regression, and validation steps. To improve the efficiency of the model and reduce the redundancy and noise of the molecular descriptors, six feature selection methods (multiple linear regression, genetic algorithms, mean impact value, forward selection, backward elimination, and +n-m algorithm) are used with the support vector machine. The best established cascaded model predicts the PCE values of DSSCs with a MAE of 0.57 (%), which is about 10% of the mean value PCE (5.62%). The validation parameters according to the OECD principles are R(2) (0.75), Q(2) (0.77), and Qcv2 (0.76), which demonstrate the great goodness-of-fit, predictivity, and robustness of the model. Additionally, the applicability domain of the cascaded QSAR model is defined for further application. This study demonstrates that the established cascaded model is able to effectively predict the PCE for organic dye sensitizers with very low cost and relatively high accuracy, providing a useful tool for the design of dye sensitizers with high PCE. © 2015 Wiley Periodicals, Inc.

  8. Developments in Quantitative Structure-Activity Relationships (QSAR). A Review

    Science.gov (United States)

    1976-07-01

    Ions 6 35 Growth . Diamines 19 35 -ydroxytenzoic • .7- 35 Esters B-Nitrostyrenes 14 84 "Pyrimidines 8 35,44 TA3 Carcinoma Antitumor Diliminopyrimi...gypsewn Growth 2,ichop;oywn Inhibition of Alkylpyrazoles 6 35 >nitzerdigitale Growth Carboxylate Ions 14 35 Diamines 22 35 Tongue Rela’ive Sweet- m...equally as well by equation 211 as are molecules with little or no ability to hydrogen bond such as naphthalene, azobenzene and chloronitrobenzene and

  9. Sensitivity Analysis of QSAR Models for Assessing Novel Military Compounds

    Science.gov (United States)

    2009-01-01

    pensive computational models and/or read-across approaches for assess- ing aquatic toxicity ( Pavan et al. 2006; Lilienblum et al. 2008). It seems likely...December 4–6, 2007, Washington, DC. Pavan , M., T. I. Netzeva, and A. P. Worth. 2006. Validation of QSAR model for toxicity. SAR Envrion. Res. 17(2

  10. Toxicity of ionic liquids: database and prediction via quantitative structure-activity relationship method.

    Science.gov (United States)

    Zhao, Yongsheng; Zhao, Jihong; Huang, Ying; Zhou, Qing; Zhang, Xiangping; Zhang, Suojiang

    2014-08-15

    A comprehensive database on toxicity of ionic liquids (ILs) is established. The database includes over 4000 pieces of data. Based on the database, the relationship between IL's structure and its toxicity has been analyzed qualitatively. Furthermore, Quantitative Structure-Activity relationships (QSAR) model is conducted to predict the toxicities (EC50 values) of various ILs toward the Leukemia rat cell line IPC-81. Four parameters selected by the heuristic method (HM) are used to perform the studies of multiple linear regression (MLR) and support vector machine (SVM). The squared correlation coefficient (R(2)) and the root mean square error (RMSE) of training sets by two QSAR models are 0.918 and 0.959, 0.258 and 0.179, respectively. The prediction R(2) and RMSE of QSAR test sets by MLR model are 0.892 and 0.329, by SVM model are 0.958 and 0.234, respectively. The nonlinear model developed by SVM algorithm is much outperformed MLR, which indicates that SVM model is more reliable in the prediction of toxicity of ILs. This study shows that increasing the relative number of O atoms of molecules leads to decrease in the toxicity of ILs. Copyright © 2014 Elsevier B.V. All rights reserved.

  11. QSAR modeling on dopamine D2 receptor binding affinity of 6-methoxy benzamides.

    Science.gov (United States)

    Samanta, Soma; Debnath, Bikash; Gayen, Shovanlal; Ghosh, Balaram; Basu, Anindya; Srikanth, Kolluru; Jha, Tarun

    2005-10-01

    QSAR modeling was performed on 58 (S) N-[(1-ethyl-2-pyrrolidinyl) methyl]-6-methoxy benzamides as dopamine (DA) D2 receptor antagonists to identify the structural requirements for DA D2 receptor binding affinity. The study pointed out that the presence of hydrophobic substituents at R3 position and electron-donating groups at R5 position increased the biological activity. Substitutions at phenyl ring favored the binding affinity of these benzamides. Ethyl group and iodine at R3 position were advantageous to the activity whereas nitro group at phenyl ring hindered the antagonistic activity.

  12. Structural insights of Staphylococcus aureus FtsZ inhibitors through molecular docking, 3D-QSAR and molecular dynamics simulations.

    Science.gov (United States)

    Ballu, Srilata; Itteboina, Ramesh; Sivan, Sree Kanth; Manga, Vijjulatha

    2018-02-01

    Filamentous temperature-sensitive protein Z (FtsZ) is a protein encoded by the FtsZ gene that assembles into a Z-ring at the future site of the septum of bacterial cell division. Structurally, FtsZ is a homolog of eukaryotic tubulin but has low sequence similarity; this makes it possible to obtain FtsZ inhibitors without affecting the eukaryotic cell division. Computational studies were performed on a series of substituted 3-arylalkoxybenzamide derivatives reported as inhibitors of FtsZ activity in Staphylococcus aureus. Quantitative structure-activity relationship models (QSAR) models generated showed good statistical reliability, which is evident from r 2 ncv and r 2 loo values. The predictive ability of these models was determined and an acceptable predictive correlation (r 2 Pred ) values were obtained. Finally, we performed molecular dynamics simulations in order to examine the stability of protein-ligand interactions. This facilitated us to compare free binding energies of cocrystal ligand and newly designed molecule B1. The good concordance between the docking results and comparative molecular field analysis (CoMFA)/comparative molecular similarity indices analysis (CoMSIA) contour maps afforded obliging clues for the rational modification of molecules to design more potent FtsZ inhibitors.

  13. Statistically validated QSARs, based on theoretical descriptors, for modeling aquatic toxicity of organic chemicals in Pimephales promelas (fathead minnow).

    Science.gov (United States)

    Papa, Ester; Villa, Fulvio; Gramatica, Paola

    2005-01-01

    The use of Quantitative Structure-Activity Relationships in assessing the potential negative effects of chemicals plays an important role in ecotoxicology. (LC50)(96h) in Pimephales promelas (Duluth database) is widely modeled as an aquatic toxicity end-point. The object of this study was to compare different molecular descriptors in the development of new statistically validated QSAR models to predict the aquatic toxicity of chemicals classified according to their MOA and in a unique general model. The applied multiple linear regression approach (ordinary least squares) is based on theoretical molecular descriptor variety (1D, 2D, and 3D, from DRAGON package, and some calculated logP). The best combination of modeling descriptors was selected by the Genetic Algorithm-Variable Subset Selection procedure. The robustness and the predictive performance of the proposed models was verified using both internal (cross-validation by LOO, bootstrap, Y-scrambling) and external statistical validations (by splitting the original data set into training and validation sets by Kohonen-artificial neural networks (K-ANN)). The model applicability domain (AD) was checked by the leverage approach to verify prediction reliability.

  14. DESIGN OF LOW CYTOTOXICITY DIARYLANILINE DERIVATIVES BASED ON QSAR RESULTS: AN APPLICATION OF ARTIFICIAL NEURAL NETWORK MODELLING

    Directory of Open Access Journals (Sweden)

    Ihsanul Arief

    2016-11-01

    Full Text Available Study on cytotoxicity of diarylaniline derivatives by using quantitative structure-activity relationship (QSAR has been done. The structures and cytotoxicities of  diarylaniline derivatives were obtained from the literature. Calculation of molecular and electronic parameters was conducted using Austin Model 1 (AM1, Parameterized Model 3 (PM3, Hartree-Fock (HF, and density functional theory (DFT methods.  Artificial neural networks (ANN analysis used to produce the best equation with configuration of input data-hidden node-output data = 5-8-1, value of r2 = 0.913; PRESS = 0.069. The best equation used to design and predict new diarylaniline derivatives.  The result shows that compound N1-(4′-Cyanophenyl-5-(4″-cyanovinyl-2″,6″-dimethyl-phenoxy-4-dimethylether benzene-1,2-diamine is the best-proposed compound with cytotoxicity value (CC50 of 93.037 μM.

  15. QSAR development and profiling of 72,524 REACH substances for PXR activation and CYP3A4 induction

    DEFF Research Database (Denmark)

    Abildgaard Rosenberg, Sine; Xia, M.; Huang, R.

    2017-01-01

    The Pregnane X Receptor (PXR) is a key regulator of enzymes, for example the cytochrome P450 isoform 3A4 (CYP3A4), and transporters involved in the metabolism and excretion of xenobiotics and endogenous compounds. Activation of PXR by xenobiotics causes altered protein expression leading to enhan......The Pregnane X Receptor (PXR) is a key regulator of enzymes, for example the cytochrome P450 isoform 3A4 (CYP3A4), and transporters involved in the metabolism and excretion of xenobiotics and endogenous compounds. Activation of PXR by xenobiotics causes altered protein expression leading...... for the remaining tens-of-thousands of man-made compounds to which humans are potentially exposed. In the present study, we used high-throughput in vitro assay results for 2816 drugs to develop four quantitative structure-activity relationship (QSAR) models with binary outputs for binding to the human PXR ligand...... binding domain, full-length human and rat PXR activation and human CYP3A4 induction, respectively. Rigorous cross- and blinded external validations demonstrated four robust and highly predictive models with balanced accuracies ranging from 75.4% to 92.7%. The models were applied to screen 72...

  16. Structural alerts for predicting clastogenic activity of pro-oxidant flavonoid compounds: quantitative structure-activity relationship study.

    Science.gov (United States)

    Yordi, Estela Guardado; Pérez, Enrique Molina; Matos, Maria Joao; Villares, Eugenio Uriarte

    2012-02-01

    Flavonoids have been reported to exert multiple biological effects that include acting as pro-oxidants at very high doses. The authors determined a structural alert to identify the clastogenic activity of a series of flavonoids with pro-oxidant activity. The methodology was based on a quantitative structure-activity relationship (QSAR) study. Specifically, the authors developed a virtual screening method for a clastogenic model using the topological substructural molecular design (TOPS-MODE) approach. It represents a useful platform for the automatic generation of structural alerts, based on the calculation of spectral moments of molecular bond matrices appropriately weighted, taking into account the hydrophobic, electronic, and steric molecular features. Therefore, it was possible to establish the structural criteria for maximal clastogenicity of pro-oxidant flavonoids: the presence of a 3-hydroxyl group and a 4-carbonyl group in ring C, the maximal number of hydroxyl groups in ring B, the presence of methoxyl and phenyl groups, the absence of a 2,3-double bond in ring C, and the presence of 5,7 hydroxyl groups in ring A. The presented clastogenic model may be useful for screening new pro-oxidant compounds. This alert could help in the design of new and efficient flavonoids, which could be used as bioactive compounds in nutraceuticals and functional food.

  17. Comparison between 5,10,15,20-tetraaryl- and 5,15-diarylporphyrins as photosensitizers: synthesis, photodynamic activity, and quantitative structure-activity relationship modeling.

    Science.gov (United States)

    Banfi, Stefano; Caruso, Enrico; Buccafurni, Loredana; Murano, Roberto; Monti, Elena; Gariboldi, Marzia; Papa, Ester; Gramatica, Paola

    2006-06-01

    The synthesis of a panel of seven nonsymmetric 5,10,15,20-tetraarylporphyrins, 13 symmetric and nonsymmetric 5,15-diarylporphyrins, and one 5,15-diarylchlorin is described. In vitro photodynamic activities on HCT116 human colon adenocarcinoma cells were evaluated by standard cytotoxicity assays. A predictive quantitative structure-activity relationship (QSAR) regression model, based on theoretical holistic molecular descriptors, of a series of 34 tetrapyrrolic photosensitizers (PSs), including the 24 compounds synthesized in this work, was developed to describe the relationship between structural features and photodynamic activity. The present study demonstrates that structural features significantly influence the photodynamic activity of tetrapyrrolic derivatives: diaryl compounds were more active with respect to the tetraarylporphyrins, and among the diaryl derivatives, hydroxy-substituted compounds were more effective than the corresponding methoxy-substituted ones. Furthermore, three monoarylporphyrins, isolated as byproducts during diarylporphyrin synthesis, were considered for both photodynamic and QSAR studies; surprisingly they were found to be particularly active photosensitizers.

  18. Introducing Catastrophe-QSAR. Application on Modeling Molecular Mechanisms of Pyridinone Derivative-Type HIV Non-Nucleoside Reverse Transcriptase Inhibitors

    Directory of Open Access Journals (Sweden)

    Marius Lazea

    2011-12-01

    Full Text Available The classical method of quantitative structure-activity relationships (QSAR is enriched using non-linear models, as Thom’s polynomials allow either uni- or bi-variate structural parameters. In this context, catastrophe QSAR algorithms are applied to the anti-HIV-1 activity of pyridinone derivatives. This requires calculation of the so-called relative statistical power and of its minimum principle in various QSAR models. A new index, known as a statistical relative power, is constructed as an Euclidian measure for the combined ratio of the Pearson correlation to algebraic correlation, with normalized t-Student and the Fisher tests. First and second order inter-model paths are considered for mono-variate catastrophes, whereas for bi-variate catastrophes the direct minimum path is provided, allowing the QSAR models to be tested for predictive purposes. At this stage, the max-to-min hierarchies of the tested models allow the interaction mechanism to be identified using structural parameter succession and the typical catastrophes involved. Minimized differences between these catastrophe models in the common structurally influential domains that span both the trial and tested compounds identify the “optimal molecular structural domains” and the molecules with the best output with respect to the modeled activity, which in this case is human immunodeficiency virus type 1 HIV-1 inhibition. The best molecules are characterized by hydrophobic interactions with the HIV-1 p66 subunit protein, and they concur with those identified in other 3D-QSAR analyses. Moreover, the importance of aromatic ring stacking interactions for increasing the binding affinity of the inhibitor-reverse transcriptase ligand-substrate complex is highlighted.

  19. Efficient dynamic molecular simulation using QSAR model to know inhibition activity in breast cancer medicine

    Science.gov (United States)

    Zharifah, A.; Kusumowardani, E.; Saputro, A.; Sarwinda, D.

    2017-07-01

    According to data from GLOBOCAN (IARC) at 2012, breast cancer was the highest rated of new cancer case by 43.3 % (after controlled by age), with mortality rated as high as 12.9 %. Oncology is a major field which focusing on improving the development of drug and therapeutics cancer in pharmaceutical and biotechnology companies. Nowadays, many researchers lead to computational chemistry and bioinformatic for pharmacophore generation. A pharmacophore describes as a group of atoms in the molecule which is considered to be responsible for a pharmacological action. Prediction of biological function from chemical structure in silico modeling reduces the use of chemical reagents so the risk of environmental pollution decreased. In this research, we proposed QSAR model to analyze the composition of cancer drugs which assumed to be homogenous in character and treatment. Atomic interactions which analyzed are learned through parameters such as log p as descriptors hydrophobic, n_poinas descriptor contour strength and molecular structure, and also various concentrations inhibitor (micromolar and nanomolar) from NCBI drugs bank. The differences inhibitor activity was observed by the presence of IC 50 residues value from inhibitor substances at various concentration. Then, we got a general overview of the state of safety for drug stability seen from its IC 50 value. In our study, we also compared between micromolar and nanomolar inhibitor effect from QSAR model results. The QSAR model analysis shows that the drug concentration with nanomolar is better than micromolar, related with the content of inhibitor substances concentration. This QSAR model got the equation: Log 1/IC50 = (0.284) (±0.195) logP + (0.02) (±0.012) n_poin + (-0.005) (±0.083) Inhibition10.2nanoM + (0.1) (±0.079) Inhibition30.5nanoM + (-0.016) (±0.045) Inhibition91.5nanoM + (-2.572) (±1.570) (n = 13; r = 0.813; r2 = 0.660; s = 0.764; F = 2.720; q2 = 0.660).

  20. Development of predictive pharmacophore model for in silico screening, and 3D QSAR CoMFA and CoMSIA studies for lead optimization, for designing of potent tumor necrosis factor alpha converting enzyme inhibitors

    Science.gov (United States)

    Murumkar, Prashant Revan; Zambre, Vishal Prakash; Yadav, Mange Ram

    2010-02-01

    A chemical feature-based pharmacophore model was developed for Tumor Necrosis Factor-α converting enzyme (TACE) inhibitors. A five point pharmacophore model having two hydrogen bond acceptors (A), one hydrogen bond donor (D) and two aromatic rings (R) with discrete geometries as pharmacophoric features was developed. The pharmacophore model so generated was then utilized for in silico screening of a database. The pharmacophore model so developed was validated by using four compounds having proven TACE inhibitory activity which were grafted into the database. These compounds mapped well onto the five listed pharmacophoric features. This validated pharmacophore model was also used for alignment of molecules in CoMFA and CoMSIA analysis. The contour maps of the CoMFA/CoMSIA models were utilized to provide structural insight for activity improvement of potential novel TACE inhibitors. The pharmacophore model so developed could be used for in silico screening of any commercial/in house database for identification of TACE inhibiting lead compounds, and the leads so identified could be optimized using the developed CoMSIA model. The present work highlights the tremendous potential of the two mutually complementary ligand-based drug designing techniques (i.e. pharmacophore mapping and 3D-QSAR analysis) using TACE inhibitors as prototype biologically active molecules.

  1. Novel Uses of In Vitro Data to Develop Quantitative Biological Activity Relationship Models for in Vivo Carcinogenicity Prediction.

    Science.gov (United States)

    Pradeep, Prachi; Povinelli, Richard J; Merrill, Stephen J; Bozdag, Serdar; Sem, Daniel S

    2015-04-01

    The availability of large in vitro datasets enables better insight into the mode of action of chemicals and better identification of potential mechanism(s) of toxicity. Several studies have shown that not all in vitro assays can contribute as equal predictors of in vivo carcinogenicity for development of hybrid Quantitative Structure Activity Relationship (QSAR) models. We propose two novel approaches for the use of mechanistically relevant in vitro assay data in the identification of relevant biological descriptors and development of Quantitative Biological Activity Relationship (QBAR) models for carcinogenicity prediction. We demonstrate that in vitro assay data can be used to develop QBAR models for in vivo carcinogenicity prediction via two case studies corroborated with firm scientific rationale. The case studies demonstrate the similarities between QBAR and QSAR modeling in: (i) the selection of relevant descriptors to be used in the machine learning algorithm, and (ii) the development of a computational model that maps chemical or biological descriptors to a toxic endpoint. The results of both the case studies show: (i) improved accuracy and sensitivity which is especially desirable under regulatory requirements, and (ii) overall adherence with the OECD/REACH guidelines. Such mechanism based models can be used along with QSAR models for prediction of mechanistically complex toxic endpoints. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  2. Structure-activity relationship studies of argiotoxins

    DEFF Research Database (Denmark)

    Poulsen, Mette H; Lucas, Simon; Bach, Tinna B

    2013-01-01

    Argiotoxin-636 (ArgTX-636), a natural product from the spider Argiope lobata, is a potent but nonselective open-channel blocker of ionotropic glutamate (iGlu) receptors. Here, three series of analogues were designed to exploit selectivity among iGlu receptors, taking advantage of a recently devel......, respectively. Thus, the first structure-activity relationship study of ArgTX-636 has been carried out and has provided lead compounds for probing the ion channel region of iGlu receptors....

  3. Artificial neural networks-based approach to design ARIs using QSAR for diabetes mellitus.

    Science.gov (United States)

    Patra, Jagdish C; Singh, Onkar

    2009-11-30

    In this article, in the first part, we propose an artificial neural network-based intelligent technique to determine the quantitative structure-activity relationship (QSAR) among known aldose reductase inhibitors (ARIs) for diabetes mellitus using two molecular descriptors, i.e., the electronegativity and molar volume of functional groups present in the main ARI lead structure. We have shown that the multilayer perceptron-based model is capable of determining the QSAR quite satisfactorily, with high R-value. Usually, the design of potent ARIs requires the use of complex computer docking and quantum mechanical (QM) steps involving excessive time and human judgement. In the second part of this article, to reduce the design cycle of potent ARIs, we propose a novel ANN technique to eliminate the computer docking and QM steps, to predict the total score. The MLP-based QSAR models obtained in the first part are used to predict the potent ARIs, using the experimental data reported by Hu et al. (J Mol Graph Mod 2006, 24, 244). The proposed ANN-based model can predict the total score with an R-value of 0.88, which indicates that there exists a close match between the predicted and experimental total scores. Using the ANN model, we obtained 71 potent ARIs out of 6.25 million new ARI compounds created by substituting different functional groups at substituting sites of main lead structure of known ARI. Finally, using high bioactivity relationship and total score values, we determined four potential ARIs out of these 71 compounds. Interestingly, these four ARIs include the two potent ARIs reported by Hu et al. (J Mol Graph Mod 2006, 24, 244) who obtained these through the complex computer docking and QM steps. This fact indicates the effectiveness of our proposed ANN-based technique. We suggest these four compounds to be the most promising candidates for ARIs to prevent the diabetic complications and further recommend for wet bench experiments to find their potential against

  4. QSAR Analysis for Some 1, 2-Benzisothiazol-3-one Derivatives as Caspase-3 Inhibitors by Stepwise MLR Method

    OpenAIRE

    Hajimahdi, Zahra; Safizadeh, Fatemeh; Zarghi, Afshin

    2016-01-01

    Caspase-3 inhibitory activities of some 1, 2-benzisothiazol-3-one derivatives were modeled by quantitative structure?activity relationship (QSAR) using stepwise-multiple linear regression (SW-MLR) method. The built model was robust and predictive with correlation coefficient (R2) of 0.91 and 0.59 for training and test groups, respectively. The quality of the model was evaluated by leave-one out (LOO) cross validation (LOO correlation coefficient, Q2) of 0.80). The results indicate that the de...

  5. Relationships between study skills and academic performance

    Science.gov (United States)

    Md Rahim, Nasrudin; Meon, Hasni

    2013-04-01

    Study skills play an important role in influencing academic performance of university students. These skills, which can be modified, can be used as an indicator on how a student would perform academically in his course of study. The purpose of the study is to determine the study skills profile among Universiti Selangor's (Unisel) students and to find the relationships of these skills with student's academic performance. A sample of seventy-eight (78) foundation studies and diploma students of Unisel were selected to participate in this study. Using Study Skills Inventory instrument, eight skills were measured. They are note taking; test taking; textbook study; concentration and memory; time management; analytical thinking and problem solving; nutrition; and vocabulary. Meanwhile, student's academic performance was measured through their current Grade Point Average (GPA). The result showed that vocabulary skill scored the highest mean with 3.01/4.00, followed by test taking (2.88), analytical thinking and problem solving (2.80), note taking (2.79), textbook study (2.58), concentration and memory (2.54), time management (2.25) and nutrition (2.21). Correlation analysis showed that test taking (r=0.286, p=0.011), note taking (r=0.224, p=0.048), and analytical thinking and problem solving (r=0.362, p=0.001) skills were positively correlated with GPA achievement.

  6. Insight into the Structural Requirements of Theophylline-Based Aldehyde Dehydrogenase lAl (ALDHlAl) Inhibitors Through Multi-QSAR Modeling and Molecular Docking Approaches.

    Science.gov (United States)

    Abdul Amin, Sk; Adhikari, Nilanjan; Gayen, Shovanlal; Jha, Tarun

    2016-01-01

    Over expression of aldehyde dehydrogenase (ALDH1A1) is one of the vital hallmarks of the self-renewal and differentiational cancer stem cells (CSCs). Till now, no selective ALDH1A1 inhibitor is commercially available in the market. So there is an urgent need to explore some novel molecules which can selectively inhibit ALDH1A1 to combat cancer. Presently, our work deals with the development of QSAR models of some theophylline-based molecules by conventional 2D-QSAR, hologram QSAR (HQSAR), and Bayesian classification modeling. The descriptors identified from these QSAR models give avenues to modulate the structure of theophylline-based compounds to a desirable biological end point. Molecular docking study reveals the selectivity of these molecules towards ALDH1A1 (PDB: 4WP7) and important binding residues (GLY 125, 458; THR 129; TRP 178; TYR 297; PHE 171, 466; VAL 174, 460; MET 175; HIS 293 etc.) for the interaction with the receptors. The current study may help to design novel compounds as selective ALDH1A1 inhibitors.

  7. Molecular docking and QSAR analyses of aromatic heterocycle thiosemicarbazone analogues for finding novel tyrosinase inhibitors.

    Science.gov (United States)

    Dong, Huanhuan; Liu, Jing; Liu, Xiaoru; Yu, Yanying; Cao, Shuwen

    2017-12-01

    A collection of 36 thiosemicarbazone analogues possessed a broad span of tyrosinase inhibitory activities was designed and obtained. Robust and reliable CoMFA and CoMSIA models were gained to predict the structure-activity relationship and the new modifier direction. Inhibitory activities of the compounds were found to greatly depend upon molecular shape, size, and charge. The sterically bulky group at the C-4 position of the thiophene ring contributed a high capacity for biological activity. Some bulky substituents at the C1-position and C12-position, and electron-negative groups at the C3-position, helped to improve the activity of these analogues. The molecular docking results provided visual evidence for QSAR analysis and detailed information about binding mode, affinity, and the principal mechanism between the ligands and tyrosinase. Based on these, a prospective structure modification and optimization of the most potent compound, T32, was suggested for further research. Copyright © 2017. Published by Elsevier Inc.

  8. Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles.

    Science.gov (United States)

    Puzyn, Tomasz; Rasulev, Bakhtiyor; Gajewicz, Agnieszka; Hu, Xiaoke; Dasari, Thabitha P; Michalkova, Andrea; Hwang, Huey-Min; Toropov, Andrey; Leszczynska, Danuta; Leszczynski, Jerzy

    2011-03-01

    It is expected that the number and variety of engineered nanoparticles will increase rapidly over the next few years, and there is a need for new methods to quickly test the potential toxicity of these materials. Because experimental evaluation of the safety of chemicals is expensive and time-consuming, computational methods have been found to be efficient alternatives for predicting the potential toxicity and environmental impact of new nanomaterials before mass production. Here, we show that the quantitative structure-activity relationship (QSAR) method commonly used to predict the physicochemical properties of chemical compounds can be applied to predict the toxicity of various metal oxides. Based on experimental testing, we have developed a model to describe the cytotoxicity of 17 different types of metal oxide nanoparticles to bacteria Escherichia coli. The model reliably predicts the toxicity of all considered compounds, and the methodology is expected to provide guidance for the future design of safe nanomaterials.

  9. QSAR OF DISTRIBUTION COEFFICIENTS FOR PU (NO3)062-COMPLEXES USING MOLECULAR MECHANICS

    Energy Technology Data Exchange (ETDEWEB)

    M. BARR; G. JARVINEN; E. MOODY

    2000-08-01

    Computer-aided modeling has been very successful in the design of chelating ligands for the formation of selective metal complexes. We report herein preliminary efforts to extend the principles developed for ion-specific chelating ligands to the weaker, more diffuse electrostatic interactions between complex anions and dicationic sites of anion-exchange resins. Calculated electrostatic affinity between plutonium (IV) hexanitrato dianions and analogue of dicationic anion-exchange sites correlate well with empirically-determined distribution coefficients. This Quantitative Structure Activity Relationship (QSAR) is useful in the determination of the overall trend within a select series of bifunctional resins and which structural modifications are most likely to be advantageous. Ultimately, we hope to refine this methodology to allow the a priori determination of ion-exchange behavior for abroad class of materials.

  10. Development of human biotransformation QSARs and application for PBT assessment refinement.

    Science.gov (United States)

    Papa, Ester; Sangion, Alessandro; Arnot, Jon A; Gramatica, Paola

    2017-04-13

    Toxicokinetics heavily influence chemical toxicity as the result of Absorption, Distribution, Metabolism (Biotransformation) and Elimination (ADME) processes. Biotransformation (metabolism) reactions can lead to detoxification or, in some cases, bioactivation of parent compounds to more toxic chemicals. Moreover, biotransformation has been recognized as a key process determining chemical half-life in an organism and is thus a key determinant for bioaccumulation assessment for many chemicals. This study addresses the development of QSAR models for the prediction of in vivo whole body human biotransformation (metabolism) half-lives measured or empirically-derived for over 1000 chemicals, mainly represented by pharmaceuticals. Models presented in this study meet regulatory standards for fitting, validation and applicability domain. These QSARs were used, in combination with literature models for the prediction of biotransformation half-lives in fish, to refine the screening of the potential PBT behaviour of over 1300 Pharmaceuticals and Personal Care Products (PPCPs). The refinement of the PBT screening allowed, among others, for the identification of PPCPs, which were predicted as PBTs on the basis of their chemical structure, but may be easily biotransformed. These compounds are of lower concern in comparison to potential PBTs characterized by large predicted biotransformation half-lives. Copyright © 2017 Elsevier Ltd. All rights reserved.

  11. Molecular modelling on small molecular CDK2 inhibitors: an integrated approach using a combination of molecular docking, 3D-QSAR and pharmacophore modelling.

    Science.gov (United States)

    Yuan, H; Liu, H; Tai, W; Wang, F; Zhang, Y; Yao, S; Ran, T; Lu, S; Ke, Z; Xiong, X; Xu, J; Chen, Y; Lu, T

    2013-10-01

    Cyclin-dependent kinase 2 (CDK2) has been identified as an important target for developing novel anticancer agents. Molecular docking, three-dimensional quantitative structure-activity relationship (3D-QSAR) and pharmacophore modelling were combined with the ultimate goal of studying the structure-activity relationship of CDK2 inhibitors. The comparative molecular similarity indices analysis (CoMSIA) model constructed based on a set of 3-aminopyrazole derivatives as CDK2 inhibitors gave statistically significant results (q (2) = 0.700; r (2) = 0.982). A HypoGen pharmacophore model, constructed using diverse CDK2 inhibitors, also showed significant statistics ([Formula: see text]Cost = 61.483; RMSD = 0.53; Correlation coefficient = 0.98). The small residues and error values between the estimated and experimental activities of the training and test set compounds proved their strong capability of activity prediction. The structural insights obtained from these two models were consistent with each other. The pharmacophore model summarized the important pharmacophoric features required for protein-ligand binding. The 3D contour maps in combination with the comprehensive pharmacophoric features helped to better interpret the structure-activity relationship. The results will be beneficial for the discovery and design of novel CDK2 inhibitors. The simplicity of this approach provides expansion to its applicability in optimizing other classes of small molecular CDK2 inhibitors.

  12. In vitro activity of natural phenolic compounds against fluconazole-resistant Candida species: a quantitative structure-activity relationship analysis.

    Science.gov (United States)

    Gallucci, M N; Carezzano, M E; Oliva, M M; Demo, M S; Pizzolitto, R P; Zunino, M P; Zygadlo, J A; Dambolena, J S

    2014-04-01

    To evaluate the antifungal activity and to analyse the structure-activity relationship of eleven natural phenolic compounds against four Candida species which are resistant to fluconazole. Four different species of Candida isolates were used: Candida albicans, Candida krusei, Candida tropicalis and Candida dubliniensis. The phenolic compound carvacrol showed the highest anti-Candida bioactivity, followed by thymol and isoeugenol. The obtained minimum inhibitory concentration (MIC) values obtained were used in a quantitative structure-activity relationship (QSAR) analysis where the electronic, steric, thermodynamic and topological descriptors served as dependent variables. According to the descriptors obtained in this QSAR study, the antifungal activity of phenols has a first action specific character which is based on their interaction with plasma or mitochondrial membranes. The second action is based on a steric descriptor-the maximal and minimal projection of the area-which could explain the inability of some phenolic compounds to be biotransformed to quinones methylene by Candida species. According to the descriptors obtained in this QSAR study, the anti-Candida activity of ortho-substituted phenols is due to more than one action mechanism. The anti-Candida activity of phenolic compounds can be predicted by their molecular properties and structural characteristics. These results could be employed to predict the anti-Candida activity of new phenolic compounds in the search for new alternatives or complementary therapies to combat against candidiasis. © 2014 The Society for Applied Microbiology.

  13. Review of Studies of Peer Relationships in Early Childhood

    OpenAIRE

    高櫻, 綾子

    2007-01-01

    This paper reviews studies of peer relationships in early childhood and examines the relation between play in young children and peer relationships. First, it focuses on playgroup entry and intimacy with peers to examine the influence of peer relationships on play. Second, it considers the influence of play on peer relationships. The results show that there is a reciprocal relationship between the two : the success of a child entering a playgroup depends on the quality of peer relationships, ...

  14. A Historical Excursus on the Statistical Validation Parameters for QSAR Models: A Clarification Concerning Metrics and Terminology.

    Science.gov (United States)

    Gramatica, Paola; Sangion, Alessandro

    2016-06-27

    In the last years, external validation of QSAR models was the subject of intensive debate in the scientific literature. Different groups have proposed different metrics to find "the best" parameter to characterize the external predictivity of a QSAR model. This editorial summarizes the history of parameter development for the external QSAR model validation and suggests, once again, the concurrent use of several different metrics to assess the real predictive capability of QSAR models.

  15. Visual analytics in cheminformatics: user-supervised descriptor selection for QSAR methods.

    Science.gov (United States)

    Martínez, María Jimena; Ponzoni, Ignacio; Díaz, Mónica F; Vazquez, Gustavo E; Soto, Axel J

    2015-01-01

    The design of QSAR/QSPR models is a challenging problem, where the selection of the most relevant descriptors constitutes a key step of the process. Several feature selection methods that address this step are concentrated on statistical associations among descriptors and target properties, whereas the chemical knowledge is left out of the analysis. For this reason, the interpretability and generality of the QSAR/QSPR models obtained by these feature selection methods are drastically affected. Therefore, an approach for integrating domain expert's knowledge in the selection process is needed for increase the confidence in the final set of descriptors. In this paper a software tool, which we named Visual and Interactive DEscriptor ANalysis (VIDEAN), that combines statistical methods with interactive visualizations for choosing a set of descriptors for predicting a target property is proposed. Domain expertise can be added to the feature selection process by means of an interactive visual exploration of data, and aided by statistical tools and metrics based on information theory. Coordinated visual representations are presented for capturing different relationships and interactions among descriptors, target properties and candidate subsets of descriptors. The competencies of the proposed software were assessed through different scenarios. These scenarios reveal how an expert can use this tool to choose one subset of descriptors from a group of candidate subsets or how to modify existing descriptor subsets and even incorporate new descriptors according to his or her own knowledge of the target property. The reported experiences showed the suitability of our software for selecting sets of descriptors with low cardinality, high interpretability, low redundancy and high statistical performance in a visual exploratory way. Therefore, it is possible to conclude that the resulting tool allows the integration of a chemist's expertise in the descriptor selection process with

  16. [Quantitative structure-activity relationship model for prediction of cardiotoxicity of chemical components in traditional Chinese medicines].

    Science.gov (United States)

    2017-06-18

    Some quantitative structure-activity relationship (QSAR) models have been developed to predict cardiac toxicity of drugs, which have limited predictive power due to based on hERG channel inhibition. The objective of this study was try to develop a QSAR model based on all kinds of cardiac adverse effects, and to predict the potential cardiotoxicity of chemical components in traditional Chinese medicines (TCM). In this study, the compounds data of all kinds of cardiac adverse reactions were selected as the training set. The QSAR models were constructed based on 1 109 compounds with cardiotoxicity and 789 compounds without cardiotoxicity, which were available from the Toxicity Reference Database (ToxRefDB) and Side Effect Resource (SIDER) database. The ADMET Predictor software was applied to calculate and to screen the molecular descriptors, and to construct the QSAR models using support vector machine (SVM) and artificial neural networks (ANN) algorithm, respectively. The models were optimized using compound-based 10-fold cross validation. Then, the predictive performance for the potential cardiotoxicity of chemical components in TCM were assessed using external validation by 19 components in TCM with cardiotoxicity and 10 components in TCM without cardiotoxicity. A total of 220 molecular descriptors were selected for modeling, and the best model using SVM algorithm contained 87 molecular descriptors. The internal validation results showed that the predictive sensitivity, specificity, the Youden's index (YI) and the Matthews correlation coefficient (MCC) were 71%, 70%, 0.41, and 0.41, respectively. The best model constructed using ANN algorithm contained 13 neurons and 87 molecular descriptors. The internal validation results showed that the predictive sensitivity, specificity, the YI and the MCC were 78%, 77%, 0.54, and 0.54, respectively. Both models were validated using external validation by the same set of 29 chemical components in TCM with or without

  17. Quantitative structure-activity relationships for green algae growth inhibition by polymer particles.

    Science.gov (United States)

    Nolte, Tom M; Peijnenburg, Willie J G M; Hendriks, A Jan; van de Meent, Dik

    2017-07-01

    After use and disposal of chemical products, many types of polymer particles end up in the aquatic environment with potential toxic effects to primary producers like green algae. In this study, we have developed Quantitative Structure-Activity Relationships (QSARs) for a set of highly structural diverse polymers which are capable to estimate green algae growth inhibition (EC50). The model (N = 43, R 2  = 0.73, RMSE = 0.28) is a regression-based decision tree using one structural descriptor for each of three polymer classes separated based on charge. The QSAR is applicable to linear homo polymers as well as copolymers and does not require information on the size of the polymer particle or underlying core material. Highly branched polymers, non-nitrogen cationic polymers and polymeric surfactants are not included in the model and thus cannot be evaluated. The model works best for cationic and non-ionic polymers for which cellular adsorption, disruption of the cell wall and photosynthesis inhibition were the mechanisms of action. For anionic polymers, specific properties of the polymer and test characteristics need to be known for detailed assessment. The data and QSAR results for anionic polymers, when combined with molecular dynamics simulations indicated that nutrient depletion is likely the dominant mode of toxicity. Nutrient depletion in turn, is determined by the non-linear interplay between polymer charge density and backbone flexibility. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. Public (Q)SAR Services, Integrated Modeling Environments, and Model Repositories on the Web: State of the Art and Perspectives for Future Development.

    Science.gov (United States)

    Tetko, Igor V; Maran, Uko; Tropsha, Alexander

    2017-03-01

    Thousands of (Quantitative) Structure-Activity Relationships (Q)SAR models have been described in peer-reviewed publications; however, this way of sharing seldom makes models available for the use by the research community outside of the developer's laboratory. Conversely, on-line models allow broad dissemination and application representing the most effective way of sharing the scientific knowledge. Approaches for sharing and providing on-line access to models range from web services created by individual users and laboratories to integrated modeling environments and model repositories. This emerging transition from the descriptive and informative, but "static", and for the most part, non-executable print format to interactive, transparent and functional delivery of "living" models is expected to have a transformative effect on modern experimental research in areas of scientific and regulatory use of (Q)SAR models. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  19. Bulimia and Interpersonal Relationships: A Longitudinal Study.

    Science.gov (United States)

    Thelen, Mark H.; And Others

    1990-01-01

    Assessed changes in bulimia in female college students (N=44) and in relation between bulimia and interpersonal relationships over time. Found (1) stable symptomology for normals and bulimics; (2) strong negative correlations between bulimia measures and interpersonal relationships with men; and (3) improvement in symptomology and relationships…

  20. The great descriptor melting pot: mixing descriptors for the common good of QSAR models.

    Science.gov (United States)

    Tseng, Yufeng J; Hopfinger, Anton J; Esposito, Emilio Xavier

    2012-01-01

    The usefulness and utility of QSAR modeling depends heavily on the ability to estimate the values of molecular descriptors relevant to the endpoints of interest followed by an optimized selection of descriptors to form the best QSAR models from a representative set of the endpoints of interest. The performance of a QSAR model is directly related to its molecular descriptors. QSAR modeling, specifically model construction and optimization, has benefited from its ability to borrow from other unrelated fields, yet the molecular descriptors that form QSAR models have remained basically unchanged in both form and preferred usage. There are many types of endpoints that require multiple classes of descriptors (descriptors that encode 1D through multi-dimensional, 4D and above, content) needed to most fully capture the molecular features and interactions that contribute to the endpoint. The advantages of QSAR models constructed from multiple, and different, descriptor classes have been demonstrated in the exploration of markedly different, and principally biological systems and endpoints. Multiple examples of such QSAR applications using different descriptor sets are described and that examined. The take-home-message is that a major part of the future of QSAR analysis, and its application to modeling biological potency, ADME-Tox properties, general use in virtual screening applications, as well as its expanding use into new fields for building QSPR models, lies in developing strategies that combine and use 1D through nD molecular descriptors.

  1. OPERA: A QSAR tool for physicochemical properties and environmental fate predictions (ACS Spring meeting)

    Science.gov (United States)

    The collection of chemical structures and associated experimental data for QSAR modeling is facilitated by the increasing number and size of public databases. However, the performance of QSAR models highly depends on the quality of the data used and the modeling methodology. The ...

  2. [Perspective of predictive toxicity assessment of in vivo repeated dose toxicity using structural activity relationship].

    Science.gov (United States)

    Ono, Atsushi

    2010-01-01

    Tens of thousands of existing chemicals have been widely used for manufacture, agriculture, household and other purposes in worldwide. Only approximately 10% of chemicals have been assessed for human health hazard. The health hazard assessment of residual large number of chemicals for which little or no information of their toxicity is available is urgently needed for public health. However, the conduct of traditional toxicity tests which involves using animals for all of these chemicals would be economically impractical and ethically unacceptable. (Quantitative) Structure-Activity Relationships [(Q)SARs] are expected as method to have the potential to estimate hazards of chemicals from their structure, while reducing time, cost and animal testing currently needed. Therefore, our studies have been focused on evaluation of available (Q)SAR systems for estimating in vivo repeated toxicity on the liver. The results from our preliminary analysis showed the distribution for LogP of the chemicals which have potential to induce liver toxicity was bell-shape and indicating the possibility to estimate liver toxicity of chemicals from their physicochemical property. We have developed (Q)SAR models to in vivo liver toxicity using three commercially available systems (DEREK, ADMEWorks and MultiCASE) as well as combinatorial use of publically available chemoinformatic tools (CDK, MOSS and WEKA). Distinct data-sets of the 28-day repeated dose toxicity test of new and existing chemicals evaluated in Japan were used for model development and performance test. The results that concordances of commercial systems and public tools were almost same which below 70% may suggest currently attainable knowledge of in silico estimation of complex biological process, though it possible to obtain complementary and enhanced performance by combining predictions from different programs. In future, the combi