quantitative structure activities relationships of some 2-mercaptoimidazoles as ccr2 inhibitors using genetic algorithm-artificial neural networks

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 pic 50 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 pic 50 s 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.