Pentopril

In this study, support vector machine (SVM) method combined with genetic algorithm (GA) for feature selection and conjugate gradient (CG) method for parameter optimization (GA-CG-SVM), has been employed to develop prediction models of human plasma protein binding rate (PPBR) and oral bioavailability (BIO). The advantage of the GA-CG-SVM is that it can deal with feature selection and SVM parameter optimization simultaneously. Five-fold cross-validation as well as independent test set method were used to validate the prediction models. For the PPBR, a total of 692 compounds were used to train and test the prediction model. The prediction accuracy by means of 5-fold cross-validation is 86% and that for the independent test set (161 compounds) is 81%. These accuracies are markedly higher over that of the best model currently available in literature. The number of descriptors selected is 29. For the BIO, the training set is composed of 690 compounds and external 76 compounds form an independent validation set. The prediction accuracy for the training set by using 5-fold cross-validation and that for the independent test set are 80% and 86%, respectively, which are better than or comparable to those of other classification models in literature. The number of descriptors selected is 25. For both the PPBR and BIO, the descriptors selected by GA-CG method cover a large range of molecular properties which imply that the PPBR and BIO of a drug might be affected by many complicated factors.

Angiotensin-converting enzyme (ACE) is known to be primarily responsible for hypertension.Three-dimensional quant. structure-activity relation (3D-QSAR) models have been constructed using the comparative mol. field anal. (CoMFA) and comparative mol. similarity indexes anal. (CoMSIA) for a series of 28 ACE inhibitors.The availability of ACE crystal structure (1UZF) provided the plausible biol. orientation of inhibitors to ACE active site (C-domain).Alignment for CoMFA obtained by docking ligands to 1UZF protein using FlexX program showed better statistical model as compared to superposition of corresponding atoms.The statistical parameters indicate reasonable models for both CoMFA (q² = 0.530, r² = 0.998) and CoMSIA (q² = 0.518, r² = 0.990).The 3D-QSAR analyses provide valuable information for the design of ACE inhibitors with potent activity towards C-domain of ACE.The group substitutions involving the Ph ring and carbon chain at the propionyl and sulfonyl moieties of captopril are essential for better activity against ACE.