Application of MIA for a QSAR Study of Inhibitory Activity of DHP Derivatives and Design of New Compounds Using WT and GA for Pixel Processing

Abstract

ABSTRACTA quantitative structure-activity relationship (QSAR) modeling was carried out for the prediction of inhibitory activity of dihydropyridine (DHP) derivatives known as calcium channel blocker (CCB) drugs. Partial least squares (PLS) algorithm was used for prediction of inhibitory activity of calcium channel antagonists as a function of the bidimensional images. In the present study, it is investigated that the effect of pixel selection by application of genetic algorithms (GAs) for PLS model, because of the GAs is very useful in the variable selection in modeling. Pre-processing methods such as wavelet transform (WT) were also used to enhance the predictive power of multivariate calibration methods. The subset of pixels, which resulted in the low prediction error, was selected by GA. To evaluate the models applied in this study (PLS, GA-PLS and WT-GA-PLS), the inhibitory activities of several compounds, not included in the modeling procedure, were predicted. The results of models showed high prediction ability with root mean square error of prediction (RMSEP) of 0.51, 0.39 and 0.17 for PLS, GA-PLS and WT-GA-PLS, respectively. The WT-GA-PLS method was employed to predict the inhibitory activity of the new antagonists.