Authors
Zhongyu Liu, Yadong Liu, Yihang Zhou, Zhiyuan Liu, Yuhang Yang, Zhiwen Wang, Tianyi Zang, Lin Wang, Yang Zhang
Published in
Communications chemistry. Jul 18, 2026. Epub Jul 18, 2026.
Abstract
Generative artificial intelligence has shown great promise in structure-based drug design (SBDD), yet existing models often suffer from a fundamental crisis of "structural hallucinations", generating molecules with high binding scores that violate basic chemical principles or physical plausibility. Here we present DrugRPG, a physicochemically-grounded 3D molecule generation framework that bridges this gap by integrating deep-learned chemical priors with fundamental physical laws. DrugRPG introduces a cross-dimensional representation alignment objective, distilling knowledge from a chemical foundation model pre-trained on 600 million molecular similarities to ensure the generation of valid topologies and realistic pharmacophoric patterns. Simultaneously, a differentiable physics-guided sampling strategy, inspired by the Lennard-Jones potential, is applied during the reverse diffusion phase to dynamically mitigate steric clashes and enforce Van der Waals compatibility. Comprehensive benchmarking demonstrates that DrugRPG reduces severe steric clashes by 65.4% compared to the state-of-the-art baseline while maintaining competitive structural self-consistency. Crucially, DrugRPG achieves a 28.6% higher success rate in generating developable candidates that satisfy multi-objective criteria, including potency, stability, and synthetic feasibility. By merging chemical heuristics with physical laws, DrugRPG effectively addresses the hallucination crisis, shifting generative SBDD from scoring-oriented optimization toward high-fidelity, realistic lead discovery.
PMID:
42471433
Bibliographic data and abstract were imported from PubMed on 19 Jul 2026.
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