QSAR study of benzofuran and indole derivatives to predict new compounds as histone lysine methyl transferase inhibitors

Abstract

Initiation and progression of several diseases by post-translational histone modifications are considered a worldwide problem. Enhancer of Zeste Homologue 2 (EZH2), which belongs to the histone-lysine N-methyl transferase (HKMT) family, has been emphasised as a promising target for cancer therapy. It is a major challenge for the scientific community to find novel approaches to treating this disease. In this study, a series of 51 derivatives of the benzofuran and indole families, previously experimentally evaluated against HKMT, was used to develop the best model with promising anticancer activity. The multiple linear regression (MLR) method, implemented in QSARINS software, was used with a genetic algorithm for variable selection. According to QSARINS, the model with two descriptors (minHBint4 and Wlambdal.unity) was found to be the best and its parameters fit well, and its validation was well established. The applicability domain was also validated for this model. Furthermore, its robustness (R2 = 0.9328), stability (Q2LOO = 0.9212, Q2LMO = 0.9187), and good predictive power (R2ext = 0.929) were also verified. Hence, this model was assumed to have predictive HKMT anticancer activity for designing active compounds. Molecular docking was also performed to identify binding interactions, and new molecules with better predicted biological activity (pIC50) were designed. The binding energy of the three designed compounds demonstrated higher binding activity at the target receptor, followed by complex stability, determined by a 100 ns molecular dynamics simulation and binding free energy calculation. Density functional theory (DFT) and pharmacokinetic analyses also confirmed their drug-like properties. Finally, it can be declared that the proposed tools allow rapid and economical identification of potential anti-HKMT drugs (anticancer drugs) for further development.