Molecular dynamics-guided receptor-dependent 4D-QSAR studies of HDACs inhibitors.

Histone deacetylases (HDACs) were highlighted as a novel category of anticancer targets. Several HDACs inhibitors were approved for therapeutic use in cancer treatment. Comparatively, receptor-dependent 4D-QSAR, LQTA-QSAR, is a new approach which generates conformational ensemble profiles of compounds by molecular dynamics simulations at binding site of enzyme. This work describes a receptor-dependent 4D-QSAR studies on hydroxamate-based HDACs inhibitors. The 4D-QSAR model was generated by multiple linear regression method of QSARINS. Leave-N-out cross-validation (LNO) and Y-randomization were performed to analysis of the independent test set and to verify the robustness of the model. Best 4D-QSAR model showed the following statistics: R2 = 0.8117, Q2LOO = 0.6881, Q2LNO = 0.6830, R2Pred = 0.884. The results may be used for further virtual screening and design for novel HDACs inhibitors. The receptor dependent 4D-QSAR model was developed for the hydroxamate derivatives as HDAC inhibitors by making use of molecular dynamics simulation to obtain conformational ensemble profile for each compound. The multiple linear regression method was used to generate 4D-QSAR model with the suitable predictive ability and the excellent statistical parameters.