Prediction of CCR5 receptor binding affinity of substituted 1-(3,3-diphenylpropyl)-piperidinyl amides and ureas based on the heuristic method, support vector machine and projection pursuit regression.

ABSTRACT

Quantitative structure-activity relationship (QSAR) models were developed to predict for CCR5 binding affinity of substituted 1-(3,3-diphenylpropyl)-piperidinyl amides and ureas using linear free energy relationship (LFER). Eight molecular descriptors selected by the heuristic method (HM) in CODESSA were used as inputs to perform multiple linear regression (MLR), support vector machine (SVM) and projection pursuit regression (PPR) studies. Compared with MLR model, the SVM and PPR models give better results with the predicted correlation coefficient (R(2)) of 0.867 and 0.834 and the squared standard error (s(2)) of 0.095 and 0.119 for the training set and R(2) of 0.732 and 0.726 and s(2) of 0.210 and 0.207 for the test set, respectively. It indicates that the SVM and PPR approaches are more adapted to the set of molecules we studied. In addition, methods used in this paper are simple, practical and effective for chemists to predict the human CCR5 chemokine receptor.