Quantitative structure activity relationship and docking studies of imidazole-based derivatives as P-glycoprotein inhibitors

Abstract

The aim of this study is to explore a quantitative structure-activity relationship (QSAR) of a set of potent imidazole-based P-gp inhibitors by different chemometric methods to predict activity of compounds. A dataset of 51 imidazole-based derivatives of P-glycoprotein (P-gp) inhibitors was collected from the literature. QSAR study of studied compounds was performed by linear model, i.e., multiple linear regression, and non-linear models, i.e., support vector machines and artificial neural network (ANN) using Dragon descriptors. Descriptors were reduced using two different methods, namely, genetic algorithm coupled partial least square and enhanced replacement method followed by stepwise regression for descriptor selection. The docking studies were carried out to investigate mode of interactions between P-gp inhibitors and P-gp. The results showed that among the different QSAR approaches applied in this work, the best model was obtained by ANN method with correlation coefficient (R-2) of 0.83 and 0.81 for training and test dataset, respectively. The results of docking study were analyzed to investigate the outlier compounds in which the proposed models could not predict activity accurately. The result of the present study is expected to be useful in the screening and activity prediction of imidazole-based P-gp inhibitors.