A QSAR study of some cyclobutenediones as CCR1 antagonists by artificial neural networks based on principal component analysis

BACKGROUND AND PURPOSE OF THE STUDY A quantitative structure activity relationship (QSAR) model based on artificial neural networks (ANN) was developed to study the activities of 29 derivatives of 3-amino-4-(2-(2-(4-benzylpiperazin-1-yl)-2-oxoethoxy) phenylamino) cyclobutenedione as C-C chemokine receptor type 1(CCR1) inhibitors. METHODS A feed-forward ANN with error back-propagation learning algorithm was used for model building which was achieved by optimizing initial learning rate, learning momentum, epoch and the number of hidden neurons. RESULTS Good results were obtained with a Root Mean Square Error (RMSE) and correlation coefficients (R(2)) of 0.189 and 0.906 for the training and 0.103 and 0.932 prediction sets, respectively. CONCLUSION The results reflect a nonlinear relationship between the Principal components obtained from calculated molecular descriptors and the inhibitory activities of the investigated molecules.