Quantitative structure activities relationships of some 2-mercaptoimidazoles as CCR2 inhibitors using genetic algorithm-artificial neural networks

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 GA-ANN can be used as a powerful chemometric tools for quantitative structure activity relationship (QSAR) studies.