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 pIC50 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.

Keywords: 2-mercaptoimidazoles; Artificial neural networks; CCR2 inhibitors; Multivariate linear regression; Quantitative structure activity relationship.

Fig. 1

Principal components analysis of the calculated descriptors of all molecules in the data set.

Fig. 2

Standardized coefficient of descriptors appeared in GA-MLR.

Fig. 3

Plots of predicted activities versus experimental activities for (A) GA-MLR, and (B) GA-ANN.

Fig. 4

Plot of standardized residuals versus leverage values (Williams plot) for (A) GA-MLR and, (B) GAANN. The compounds included in the training and test sets, are denoted differently; the response outliers and structurally influential compounds, explained in the text, are denoted using numbers. The horizontal lines are the 2.0σ limit and the vertical one is the warning value of leverage (h* = 0.470).

Fig. 5

Mesh counter plots of output of GA-ANN (on the basis of RMSECV) to optimize networks parameters including linear rate, momentum, and number of hidden layer nodes (n_H ) (A) n_H = 2; (B) n_H = 3; (C) n_H = 4; (D) n_H = 5; (E) n_H = 6; and (F) n_H =7.

Fig. 6

Plot of RMSECV for training set versus the number of iterations