Integrating docking scores and key interaction profiles to improve the accuracy of molecular docking: towards novel B-RafV600E inhibitors

Abstract

A set of ninety-eight B-Raf^V600E inhibitors was used for the development of a molecular docking based QSAR model using linear and non-linear regression models. The integration of docking scores and key interaction profiles significantly improved the accuracy of the QSAR models, providing reasonable statistical parameters (R_train² = 0.935, R_test² = 0.728 and Q_CV² = 0.905). The established MD-SVR (molecular docking based SMV regression) model as well as model screening of a natural product database was carried out and two natural products (quercetin and myricetin) with good prediction activities were biologically evaluated. Both compounds exhibited promising B-Raf^V600E inhibitory activities (IC^Quercetin₅₀ = 7.59 μM and IC^Myricetin₅₀ = 1.56 μM), suggesting a high reliability and good applicability of the established MD-SVR model in the future development of B-Raf^V600E inhibitors with high efficacy.