ArticleActa pharmacologica Sinica2026
AI-enhanced virtual screening approach to hit identification for GluN1/GluN3A NMDA receptor.
Article in Acta pharmacologica Sinica, 2026. 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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2 citing papers in PubMed.
- A hybrid high activity aware framework integrating graph attention network and transformer for half maximal inhibitory concentration prediction.Frontiers in immunology · 2026Article
- Histone lactylation: a new target for overcoming immune evasion and therapy resistance.Medical oncology (Northwood, London, England) · 2025Review
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10 authors.
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Abstract
N-methyl-D-aspartate receptors (NMDARs) are calcium-permeable ionotropic glutamate receptors broadly expressed throughout the central nervous system, where they play crucial roles in neuronal development and synaptic plasticity. Among the various subtypes, the GluN1/GluN3A receptor represents a unique glycine-gated NMDAR with notably low calcium permeability. Despite its distinctive properties, GluN1/GluN3A remains understudied, particularly with respect to pharmacological tools development. This scarcity poses challenges for deeper investigation into its physiological functions and therapeutic relevance. In this study, we employed a hybrid virtual screening (VS) pipeline that integrates ligand-based and structure-based approaches for the efficient and precise identification of small-molecule candidates targeting GluN1/GluN3A. A large compound library comprising 18 million molecules was screened using an AI-enhanced multi-stage method. The initial phase utilized shape similarity ranking via ROCS-BART, followed by refinement with a graph neural network (GNN)-based drug-target interaction model to enhance docking accuracy. Functional validation using calcium flux (FDSS/μCell) identified two compounds with IC
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