Predictive QSAR modeling of CCR5 antagonist piperidine derivatives using chemometric tools

Abstract

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 (Q2 = 0.514) but the best externally validated model was obtained with PLS ( = 0.565). When structural, physicochemical, spatial and electronic descriptors were used, the best Q2 value (0.562) was obtained from the stepwise regression derived model whereas the best 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 Q2 and 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 point of external predictability, selected ANN models were superior to the linear QSAR models. All reported models satisfy the criteria of external validation as recommended by Golbraikh and Tropsha (J Mol Graphics Mod 2002; 20: 269–276), whereas the majority of the models have modified r2 () value of the test set for external validation more than 0.5 as suggested by Roy and Roy (QSAR Comb Sci 2008; 27: 302-313).