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.