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Application of Pc-Ann and Pc-Ls-Svm in Qsar of Ccr1 Antagonist Compounds: A Comparative Study Publisher Pubmed



Shahlaei M1, 2 ; Fassihi A1, 3 ; Saghaie L1, 3
Authors
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Authors Affiliations
  1. 1. Department of Medicinal Chemistry, Faculty of Pharmacy, Isfahan University of Medical Sciences, 81746-73461 Isfahan, Iran
  2. 2. Department of Medicinal Chemistry, Faculty of Pharmacy, Kermanshah University of Medical Sciences, Kermanshah, Iran
  3. 3. Isfahan Pharmaceutical Sciences Research Center, 81746-73461 Isfahan, Iran

Source: European Journal of Medicinal Chemistry Published:2010


Abstract

Principal component regression (PCR), principal component-artificial neural network (PC-ANN), and principal component-least squares-support vector machine (PC-LS-SVM) as regression methods were investigated for building quantitative structure-activity relationships for the prediction of inhibitory activity of some CCR1 antagonists. Nonlinear methods (PC-ANN and PC-LS-SVM) were better than the PCR method considerably in the goodness of fit and predictivity parameters and other criteria for evaluation of the proposed model. These results reflect a nonlinear relationship between the principal components obtained from molecular descriptors and the inhibitory activity of this set of molecules. The maximum variance in activity of the molecules, in PCR method was 45.5%, whereas nonlinear methods, PC-ANN and PC-LS-SVM, could explain more than 93.7% and 95.6% variance in activity data respectively. © 2010 Elsevier Masson SAS. All rights reserved.
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