A Site‐Aware Representation Learning Framework For Unified Molecular Interaction Modeling and Generative Design
TL;DR
MolDBG achieves competitive performance across all three tasks while enabling site‐specific affinity prediction and interpretable binding‐site discovery, and generalizes to structurally elusive targets, including cryptic pockets and intrinsically disordered proteins.
Abstract
ABSTRACT 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.