ArticleActa pharmacologica Sinica2025
An efficient deep learning-based strategy to screen inhibitors for GluN1/GluN3A receptor.
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.
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Abstract
The GluN1/GluN3A receptor, a unique excitatory glycine receptor recently identified in the central nervous system, challenges traditional perspectives of N-methyl-D-aspartate (NMDA) receptor diversity and glycinergic signaling. Its role in emotional regulation positions it as a potential therapeutic target for neuropsychiatric disorders. However, pharmacological research on GluN1/GluN3A receptors remains at an early stage. Traditional high-throughput screening methods for ion channel drug discovery often lack efficiency, particularly when applied to large compound libraries. To address this concern, we designed a deep learning-based strategy that balances efficiency and accuracy for identifying GluN1/GluN3A inhibitors. First, a sequence-based scoring function was developed to rapidly screen a library containing 18 million compounds, reducing the pool to approximately 10
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