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

Authors

Gang Luo, Qianqian Zhang, Chenhao Wang, Zhiheng Yi, Alex Jinpeng Wang, Jing Tang, Min Li

Published in

Advanced science (Weinheim, Baden-Wurttemberg, Germany). Pages e77238. Sep 10, 2026. Epub Sep 10, 2026.

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.

PMID:
42723155
Bibliographic data and abstract were imported from PubMed on 11 Sep 2026.

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