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

作者：Shahlaei M; Fassihi A^*; Saghaie L; Arkan E; Pourhossein A

来源：Daru-Journal of Faculty of Pharmacy, 2011, 19(5): 376-384.

• Quantitative Structure Activity Relationship
• inhibitory activity
• feed-forward ANN
• PCA

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 -y1)-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.

• 出版日期2011