Author/Authors :
Shahlaei, M. kermanshah university of medical sciences - Faculty of Pharmacy - Department of Medicinal Chemistry, كرمانشاه, ايران , Shahlaei, M. isfahan university of medical sciences - School of Pharmacy and Isfahan Pharmaceutical Sciences Research Center - Department of Medicinal Chemistry, اصفهان, ايران , Fassihi, A. isfahan university of medical sciences - School of Pharmacy and Isfahan Pharmaceutical Sciences Research Center - Department of Medicinal Chemistry, اصفهان, ايران , Saghaie, L. kermanshah university of medical sciences - Faculty of Pharmacy - Department of Medicinal Chemistry, كرمانشاه, ايران , Arkan, E. tehran university of medical sciences tums - School of Advanced Medical Technologies - Department of Medical Nanotechnology, تهران, ايران , Pourhossein, A. islamic azad university - Department of chemical engineering, ايران
Abstract :
Background and the 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 (R2) 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.
Keywords :
Quantitative Structure Activity Relationship , inhibitory activity , feed , forward ANN , PCA.
