Authors
Haoyu Wang, Ziyan Zhang, Swee Hong Ng, Haipeng Gong
Published in
Advanced science (Weinheim, Baden-Wurttemberg, Germany). Pages e77997. Oct 18, 2026. Epub Oct 18, 2026.
Abstract
The advancement of artificial intelligence (AI) has reshaped drug discovery. AI-based models typically rely on molecular representations for prediction. However, the absence of physically grounded information in mainstream molecular representations not only limits the model performance in practical applications but also hinders mechanistic understanding and exploitation by humans. To overcome this issue, we introduce qcGEM, a quantum-chemistry-aware graph-based embedding of molecules that incorporates physical priors into molecular representation learning. Through a specially designed model architecture to adequately process quantum chemistry information, qcGEM provides a compact, physics-informed molecular representation that supports a diverse range of downstream applications. Particularly, qcGEM demonstrates state-of-the-art performance across a broad range of molecule-related benchmarks, as evidenced by comprehensive evaluations on 83 tasks including molecular property prediction, activity cliff detection, protein-ligand interaction modeling, and opioid drug regression, and simultaneously offers strong interpretability at multiple representation levels. We additionally propose a simplified variant, qcGEM-Hybrid, with substantially accelerated embedding generation and robust performance. Overall, our method provides an advanced molecular representation that will benefit molecule-related modeling and prediction, supporting further progress in AI-aided drug discovery.
PMID:
42893405
Bibliographic data and abstract were imported from PubMed on 18 Oct 2026.
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