Integrating machine learning and molecular simulations for the design of potent HDAC2 inhibitors in diffuse large B-cell lymphoma.

Histone deacetylase 2 (HDAC2) plays a critical role in the pathogenesis of diffuse large B-cell lymphoma (DLBCL), positioning it as an attractive therapeutic target. In this study, we applied an integrated computational strategy that combined machine learning-based quantitative structure-activity relationship (QSAR) modelling, molecular docking, ADMET filtering, and molecular dynamics (MD) simulations to identify and optimize potential HDAC2 inhibitors. A dataset of 1995 HDAC2-active molecules was assembled and reduced to 25 key molecular descriptors, enabling the development of a robust Random Forest QSAR model (R2 = 0.926, CCC = 0.956). Top-predicted compounds underwent docking and pharmacokinetic evaluation, highlighting compound 10 as a promising lead (binding energy ≈ -10.2 kcal·mol-1). Guided optimization produced analogue 10 g, which displayed enhanced affinity (-10.9 kcal·mol-1), stable protein-ligand interactions in MD simulations, and favourable MM-GBSA binding free energy (ΔG_bind ≈ -40.3 kcal·mol-1). In silico pharmacokinetic analysis further suggested acceptable safety and drug-likeness. This multi-stage pipeline prioritizes 10 g as a strong candidate for experimental validation and demonstrates the value of integrating predictive modelling with structure-based refinement in anti-lymphoma drug discovery.