In silico evaluation, molecular docking and QSAR analysis of quinazoline-based EGFR-T790M inhibitors

Abstract

Mutated epidermal growth factor receptor (EGFR-T790M) inhibitors hold promise as new agents against cancer. Molecular docking and QSAR analysis were performed based on a series of fifty-three quinazoline derivatives to elucidate key structural and physicochemical properties affecting inhibitory activity. Molecular docking analysis identified the true conformations of ligands in the receptor’s active pocket. The structural features of the ligands, expressed as molecular descriptors, were derived from the obtained docked conformations. Non-linear and spline QSAR models were developed through novel genetic algorithm and artificial neural network (GA-ANN) and multivariate adaptive regression spline techniques, respectively. The former technique was employed to consider non-linear relation between molecular descriptors and inhibitory activity of quinazoline derivatives. The later technique was also used to describe the non-linearity using basis functions and sub-region equations for each descriptor. Our QSAR model gave a high predictive performance $(R_{\mathrm{p}}^{2}=0.881, Q_{\mathrm{LOO}}^{2}=0.923, R^{2}_{\mathrm{LSO}}=0.828$ and $r_{\mathrm{m}}^{2}=0.772$) using diverse validation techniques. Eight new compounds were designed using our QSAR model as potent EGFR-T790M inhibitors. Overall, the proposed in silico strategy based on docked derived descriptor and non-linear descriptor subset selection may help design novel quinazoline derivatives with improved EGFR-T790M inhibitory activity.