ArticleActa pharmacologica Sinica2025
Discovery of novel GluN1/GluN3A NMDA receptor inhibitors using a deep learning-based method.
Article in Acta pharmacologica Sinica, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- New advances in small molecule drugs targeting NMDA receptors.Acta pharmacologica Sinica · 2026Review
- AI-driven breakthroughs in ion channel drug discovery: the future is now.Acta pharmacologica Sinica · 2026Article
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Authors and funding
10 authors.
Funding
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Abstract
Ligand-based drug discovery methods typically utilize pharmacophore similarities among molecules to screen for potential active compounds. Among these, scaffold hopping is a widely used ligand-based lead identification strategy that facilitates clinical candidate discovery by seeking inhibitors with similar biological activity yet distinct scaffolds. In this study, we employed GeminiMol, a deep learning-based molecular representation framework that incorporates bioactive conformational space information. This approach enables ligand-based virtual screening by referencing known active compounds to identify potential hits with similar structural and bioactive conformational features. Using GeminiMol-based ligand screening method, we discovered a potent GluN1/GluN3A inhibitor, GM-10, from an 18-million-compound library. Notably, GM-10 features a completely different scaffold compared to known inhibitors. Subsequent validation using whole-cell patch-clamp recording confirmed its activity, with an IC
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