RESUMEN
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. 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. 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. The results reflect a nonlinear relationship between the Principal components obtained from calculated molecular descriptors and the inhibitory activities of the investigated molecules