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A QSAR Study of Some Cyclobutenediones as CCR1 Antagonists by Artificial Neural Networks Based on Principal Component Analysis

Author(s): M Shahlaei | A Fassihi | L Saghaie | E Arkan | A Pourhossein

Journal: DARU : Journal of Pharmaceutical Sciences
ISSN 1560-8115

Volume: 19;
Issue: 5;
Start page: 376;
Date: 2011;
Original page

Keywords: 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-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.

Tango Rapperswil
Tango Rapperswil

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