AI-driven virtual screening platform identifies novel NSUN2 inhibitor candidates for targeted cancer therapy: a computational drug discovery approach.

The RNA cytosine-5 methyltransferase NSUN2 is an emerging therapeutic target in precision oncology, with aberrant overexpression driving tumor progression, metastasis, and therapy resistance across multiple malignancies. Despite its critical role in cancer biology, selective small-molecule inhibitors remain limited. We employed an AI-accelerated workflow to screen approximately 101 million compounds from the ZINC database using structure-based virtual screening. The AlphaFold2-predicted human NSUN2 structure was aligned with the experimentally determined M. jannaschii TRM4 homolog (PDB: 3A4T, 34.2% sequence identity, 1.82 Å RMSD). A CatBoost ensemble classifier trained on Morgan fingerprint descriptors with AutoDock Vina-derived labels achieved robust performance (training: recall 0.87, ROC-AUC 0.89; test: recall 0.71, ROC-AUC 0.85), with low test precision reflecting extreme class imbalance inherent to virtual screening. Multi-stage filtering identified 12,000 high-scoring compounds with binding affinities of -9.933 to -8.375 kcal/mol. ADMET profiling yielded 34 drug-like candidates with favorable pharmacokinetic and toxicological profiles. Molecular dynamics simulations over 50 nanoseconds validated binding stability of lead compounds ZINC-1000507789 and ZINC-1000507824. These structurally diverse non-covalent reversible inhibitors targeting the SAM cofactor binding pocket warrant experimental validation through biochemical assays and cellular studies to overcome therapeutic resistance in NSUN2-driven malignancies.