Enhancing Virtual Screening of Cystathionine β-Synthase Inhibitors: Benchmarking Target-Specific Machine-Learning Scoring Functions Against State-of-the-Art AI Docking and Co-Folding Approaches

Cystathionine beta-synthase (CBS) has emerged as an important therapeutic target implicated in cancer and Down syndrome, yet the discovery of selective CBS inhibitors remains challenging due to limited structural diversity of known ligands and the scarcity of target-focused virtual screening (VS) benchmarks. In this study, we present the first comprehensive evaluation of CBS-specific machine-learning (ML) models for structure-based VS, supported by a carefully curated and up-to-date data set of experimentally validated CBS inhibitors, true inactives and decoys. Importantly, we benchmarked their performance against a diverse panel of 16 established VS pipelines, including classical docking-based scoring schemes, modern deep-learning (DL) docking tools, and recent co-folding approaches for protein-ligand modeling and affinity prediction. The CBS-specific ML classifiers substantially outperformed these state-of-the-art (SOTA) methods in early enrichment, highlighting the advantage of target-focused training for VS tasks. Our results further reveal that generic DL docking and co-folding approaches struggle to achieve reliable screening performance when applied to targets that are under-represented in or entirely absent from their training data, as appears to be the case for CBS. This underscores a key limitation of broadly trained foundation-style models in prospective drug discovery campaigns involving less-studied proteins.