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Quantitative Structure Activities Relationships of Some 2-Mercaptoimidazoles As Ccr2 Inhibitors Using Genetic Algorithm-Artificial Neural Networks



Saghaie L1 ; Shahlaei M2 ; Fassihi A1
Authors
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Authors Affiliations
  1. 1. Department of Medicinal Chemistry, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, Iran
  2. 2. Department of Medicinal Chemistry, Faculty of Pharmacy, Kermanshah University of Medical Sciences, Kermanshah, Iran

Source: Research in Pharmaceutical Sciences Published:2013

Abstract

Quantitative relationships between structures of twenty six of 2-mercaptoimidazoles as C-C chemokine receptor type 2 (CCR2) inhibitors were assessed. Modeling of the biological activities of compounds of interest as a function of molecular structures was established by means of genetic algorithm multivariate linear regression (GA-MLR) and genetic algorithm (GA-ANN). The results showed that, the pIC50 values calculated by GA-ANN are in good agreement with the experimental data, and the performance of the artificial neural networks regression model is superior to the multivariate linear regression-based (MLR) model. With respect to the obtained results, it can be deduced that there is a non-linear relationship between the pIC50s and the calculated structural descriptors of the 2-mercaptoimidazoles. The obtained models were able to describe about 78% and 93% of the variance in the experimental activity of molecules in training set, respectively. The study provided a novel and effective approach for predicting biological activities of 2- mercaptoimidazole derivatives as CCR2 inhibitors and disclosed that combined genetic algorithm and GAANN can be used as a powerful chemometric tools for quantitative structure activity relationship (QSAR) studies.
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