Moreover, drug discovery remains a prolonged and expensive process characterized by a notably low (13.8%) success rate . The integration of machine learning (ML) technologies into drug discovery research emerges as a crucial avenue to address these challenges. Numerous ML models focused on small-molecule design and quantitative structure-activity relationship (QSAR) have been specifically designed for virtual screening (VS) of drugs against targets. These innovations represent pioneering steps toward reducing overall costs and timelines in drug discovery research. Various available computer-aided drug design (CADD) and screening programs such as Schrödinger CovDock , DUckCov , Cov_DOX , WIDOCK , Reactive Docking , and BIreactive , specialize in modeling close contacts and predicting the binding mode of covalent ligands or the reactivity of warheads against a model sidechain.