QSAR Modeling of 1-(3,3-Diphenylpropyl)-Piperidinyl Amides as CCR5 Modulators Using Multivariate Adaptive Regression Spline and Bayesian Regularized Genetic Neural Networks

This study deals with developing a quantitative structure-activity relationship (QSAR) model for describing and predicting the inhibition activity of 1-(3,3-diphenylpropyl)-piperidinyl derivatives as CCR5 modulators. Applying the multiple linear regressions (MLR) and its inability in predicting the inhibition behavior showed that the interaction has no linear characteristics. To assess the nonlinear characteristics of the inhibition activity artificial neural networks (ANN) was used for data modeling. In order to select the variables needed for developing ANNs, three variable selection algorithms were used: Stepwise-MLR, genetic algorithm-partial least squares (GA-PLS), and Bayesian regularized genetic neural networks (BRGNNs). R-2 and root mean square error (RMSE) values for training (t) and leave-one-out (LOO) procedures revealed that BRGNNs is a robust algorithm for the variable selection and regression method simultaneously. Due to the 'black box' limitation of neural networks, multivariate adaptive regression spline (MARS) technique was used for modeling. A prominent advantage of MARS with 2 respect to ANN is its ability in interpreting of the results of the model. Q(LOO)(2) and R-t(2) (0.982 and 0.947) reveal that MARS can describe and predict inhibition activity of these modulators and is as robust as ANN. Because the MARS model can explain the activity of molecules, it is a useful model for designing novel CCR5 inhibitors.