A Site-Aware Representation Learning Framework For Unified Molecular Interaction Modeling and Generative Design

Unifying drug-target affinity prediction and targeted molecular design within a single interpretable framework remains challenging. Many sequence-based affinity and design methods rely on global target representations without explicitly modeling binding regions, leading to site-level ambiguity in both screening and design. By contrast, structure-based methods require high-quality structural data and are poorly suited to dynamic targets. In this study, a new model named MolDBG is proposed as a unified site-aware framework that combines affinity prediction, binding-site identification, and affinity-conditioned molecular generation within a single architecture. With binding-site supervision, MolDBG prioritizes interaction-critical residues before learning drug-target representations, reducing false positives from misaligned binding sites and improving interpretability. MolDBG achieves competitive performance across all three tasks while enabling site-specific affinity prediction and interpretable binding-site discovery. The framework generalizes to structurally elusive targets, including cryptic pockets and intrinsically disordered proteins. Overall, these results demonstrate that MolDBG is a promising framework for molecular design and screening.