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External Test Set (external + test_set)
Selected AbstractsConceptual DFT properties-based 3D QSAR: Analysis of inhibitors of the nicotine metabolizing CYP2A6 enzymeJOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 12 2009Sofie Van Damme Abstract Structure-activity relationships of 46 P450 2A6 inhibitors were analyzed using the 3D-QSAR methodology. The analysis was carried out to confront the use of traditional steric and electrostatic fields with that of a number of fields reflecting conceptual DFT properties: electron density, HOMO, LUMO, and Fukui f, function as 3D fields. The most predictive models were obtained by combining the information of the electron density with the Fukui f, function (r2 = 0.82, q2 = 0.72), yielding a statistically significant and predictive model. The generated model was able to predict the inhibition potencies of an external test set of five chemicals. The result of the analysis indicates that conceptual DFT-based molecular fields can be useful as 3D QSAR molecular interaction fields. © 2008 Wiley Periodicals, Inc. J Comput Chem 2009 [source] Application of QSPR to Binary Polymer/Solvent Mixtures: Prediction of Flory-Huggins ParametersMACROMOLECULAR THEORY AND SIMULATIONS, Issue 9 2008Jie Xu Abstract A QSPR study was performed for the prediction of the Flory-Huggins parameters of binary polymer/solvent mixtures. 1,664 descriptors for each polymer and solvent were checked and a cubic multivariable model, with R2,=,0.9638 and s,=,0.146, was produced by using genetic algorithms on a training set of 52 mixtures. The reliability of the proposed model was further validated by satisfactory statistical parameters being obtained using an external test set (,=,0.9565). All descriptors involved in the model can be derived solely from the chemical structures of the polymers and the solvents, which makes it very useful in predicting the Flory-Huggins parameters of unknown or unavailable polymer/solvent mixtures. [source] Exploring QSAR for Substituted 2-Sulfonyl-Phenyl-Indol Derivatives as Potent and Selective COX-2 Inhibitors Using Different Chemometrics ToolsCHEMICAL BIOLOGY & DRUG DESIGN, Issue 6 2008Mehdi Khoshneviszadeh Selective inhibition of cyclooxygenase-2 inhibitors is an important strategy in designing of potent anti-inflammatory compounds with significantly reduced side effects. The present quantitative structure,activity relationship study, attempts to explore the structural and physicochemical requirements of 2-sulfonyl,phenyl,indol derivatives (n = 30) for COX-2 inhibitory activity using chemical, topological, geometrical, and quantum descriptors. Some statistical techniques like stepwise regression, multiple linear regression with factor analysis as the data preprocessing (FA-MLR), principal component regression analysis, and genetic algorithms partial least squares analysis were applied to derive the quantitative structure,activity relationship models. The generated equations were statistically validated using cross-validation and external test set. The quality of equations obtained from stepwise multiple linear regression, FA-MLR, principal component regression analysis and PLS were in the acceptable statistical range. The best multiple linear regression equation obtained from factor analysis (FA-MLR) as the preprocessing step could predict 77.5% of the variance of the cyclooxygenase-2 inhibitory activity whereas that derived from genetic algorithms partial least squares could predict 84.2% of variances. The results of quantitative structure,activity relationship models suggested the importance of lipophilicity, electronegativity, molecular area and steric parameters on the cyclooxygenase-2 inhibitory activity. [source] Prospective Validation of a Comprehensive In silico hERG Model and its Applications to Commercial Compound and Drug DatabasesCHEMMEDCHEM, Issue 5 2010Munikumar Abstract Ligand-based in silico hERG models were generated for 2,644 compounds using linear discriminant analysis (LDA) and support vector machines (SVM). As a result, the dataset used for the model generation is the largest publicly available (see Supporting Information). Extended connectivity fingerprints (ECFPs) and functional class fingerprints (FCFPs) were used to describe chemical space. All models showed area under curve (AUC) values ranging from 0.89 to 0.94 in a fivefold cross-validation, indicating high model consistency. Models correctly predicted 80,% of an additional, external test set; Y-scrambling was also performed to rule out chance correlation. Additionally models based on patch clamp data and radioligand binding data were generated separately to analyze their predictive ability when compared to combined models. To experimentally validate the models, 50 of the predicted hERG blockers from the Chembridge database and ten of the predicted non-hERG blockers from an in-house compound library were selected for biological evaluation. Out of those 50 predicted hERG blockers, tested at a concentration of 10,,M, 18 compounds showed more than 50,% displacement of [3H]astemizole binding to cell membranes expressing the hERG channel. Ki values of four of the selected binders were determined to be in the micromolar and high nanomolar range (Ki (VH01)=2.0,,M, Ki (VH06)=0.15,,M, Ki (VH19)=1.1,,M and Ki (VH47)=18 ,M). Of these four compounds, VH01 and VH47 showed also a second, even higher affinity binding site with Ki values of 7.4,nM and 36,nM, respectively. In the case of non-hERG blockers, all ten compounds tested were found to be inactive, showing less than 50,% displacement of [3H]astemizole binding at 10,,M. These experimentally validated models were then used to virtually screen commercial compound databases to evaluate whether they contain hERG blockers. 109,784 (23,%) of Chembridge, 133,175 (38,%) of Chemdiv, 111,737 (31,%) of Asinex and 11,116 (18,%) of the Maybridge database were predicted to be hERG blockers by at least two of the models, a prediction which could, for example, be used as a pre-filtering tool for compounds with potential hERG liabilities. [source] | ||||||||||