Enhancing structure-based virtual screening of MDM2–p53 inhibitors: a benchmark of machine learning vs. traditional docking scoring functions

Abstract

The interaction between p53 and MDM2 represents a key therapeutic target in several cancers where MDM2 overexpression suppresses p53 activity. Despite extensive research, the discovery of potent and selective MDM2 inhibitors remains challenging, underscoring the need for computational strategies specifically designed for this target. In this study, we developed a machine learning\x{2013}based approach to improve structure-based virtual screening (SBVS) for identifying MDM2 inhibitors at the p53 binding site. The models were developed, trained, and tested using experimentally validated bioactivity data from ChEMBL and PubChem, complemented with challenging decoy compounds to enhance predictive accuracy. Protein\x{2013}ligand interactions were encoded using Protein\x{2013}Ligand Extended Connectivity (PLEC) and Grid fingerprints, and multiple machine learning algorithms were evaluated. Among the implemented models, PLEC\x{2013}Random Forest and PLEC\x{2013}Support Vector Machine achieved the highest predictive performance, outperforming commonly used structure-based scoring functions, including Smina, CNN-Score, and SCORCH. Overall, these ML-based scoring functions enhance the in-silico identification of MDM2 inhibitors and provide a practical framework to guide future experimental validation and drug repurposing strategies for cancers driven by MDM2 overexpression.