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A Qsar Study of Some Cyclobutenediones As Ccr1 Antagonists by Artificial Neural Networks Based on Principal Component Analysis



Shahlaei M1 ; Fassihi A2 ; Saghaie L1 ; Arkan E3 ; Pourhossein A4
Authors
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Authors Affiliations
  1. 1. Department of Medicinal Chemistry, Faculty of Pharmacy, Kermanshah University of Medical Sciences, Kermanshah, Iran
  2. 2. Department of Medicinal Chemistry, School of Pharmacy and Isfahan Pharmaceutical Sciences Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
  3. 3. Department of Medical Nanotechnology, School of Advanced Medical Technologies, Tehran University of Medical Sciences, Tehran, Iran
  4. 4. Department of chemical engineering, Science and Research branch, Islamic Azad university, Kermanshah, Iran

Source: DARU, Journal of Pharmaceutical Sciences Published:2011

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 (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.
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