QSAR study of some CCR5 antagonists as anti-HIV agents using radial basis function neural network and general regression neural network on the basis of principal components

Quantitative relationships between molecular structures and bioactivities of a set of CCR5 inhibitor derivatives were discovered. We have demonstrated the detailed application of two efficient nonlinear methods, general regression and radial basis function neural networks, for evaluation of quantitative structure-activity relationships of the studied compounds. Components produced by principal component analysis were used as input of the developed nonlinear models. Comparison between predictability of PC-GRNNs and PC-RBFNNs indicated that later method has higher ability to predict the activity of the studied molecules. In order to design novel derivatives of inhibitors with high activity and low side effects, and because experimental and calculated activities of molecules employed in the model development step, shown a good correlation, developed PC-RBFNNs QSAR model was used to calculate inhibitory activities of some suggested compounds.