Integrating machine learning and molecular simulations for the design of potent HDAC2 inhibitors in diffuse large
Josiah Joseph Isah1, Adamu Uzairu2, Sani Uba2
1Department of Chemistry, Faculty of Sciences, Federal University of Health Sciences Otukpo, Nigeria; Department of Chemistry, Faculty of Physical Sciences, Ahmadu Bello University Zaria, Nigeria.
Abstract:
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.
