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AI-enhanced virtual screening approach to hit identification for GluN1/GluN3A NMDA receptor

Acta Pharmacologica Sinica volume 47, pages 41–52 (2026)Cite this article

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 IC50 values below 10 μM. Of these, one candidate exhibited potent inhibitory activity with an IC50 of 5.31 ± 1.65 μM, which was further confirmed through manual patch-clamp recordings. These findings highlight an AI-enhanced VS workflow that achieves both efficiency and precision, providing a promising framework for exploring elusive targets such as GluN1/GluN3A.

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Fig. 1: AI-enhanced virtual screening workflow.
Fig. 2: Diversity analysis of ROCS scoring.
Fig. 3: Workflow for ROCS scoring prediction using a pretrained unsupervised model.
Fig. 4: GluN1/GluN3A structure and potential allosteric binding sites.
Fig. 5: The performance of the ROCS-BART model in screening.
Fig. 6: Inhibitory activity and predicted binding mode of LP-2 on GluN1/GluN3A receptor.

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Acknowledgements

This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant ID: XDB0830403) and the National Science and Technology Innovation 2030 Major Program (Grant ID: 2021ZD0200900). The authors gratefully acknowledge Professor Mao-lin Wang from the First Affiliated Hospital of Shantou University Medical College for his invaluable technical support and assistance with the OpenEye software.

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Author notes

  1. These authors contributed equally: Yue-shan Ji, Yue Zeng, Shao-fei Hu

Authors and Affiliations

  1. Lepu Medical Technology (Beijing) Co., Ltd, Beijing, 102200, China

    Yue-shan Ji, Shao-fei Hu, Shu-wang Li, Bei-chen Zhang, Chang Liu & Yue Kong

  2. State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China

    Yue Zeng, Hao-chen Wu, An-yang Wang & Zhao-bing Gao

  3. University of Chinese Academy of Sciences, Beijing, 100049, China

    Yue Zeng & Zhao-bing Gao

  4. Department of Pharmacology, School of Pharmacy, Fudan University, Shanghai, 200032, China

    Yue Zeng

  5. Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan, 528400, China

    Zhao-bing Gao

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Contributions

YK and ZBG designed the study. YK, YSJ, SFH, BCZ, SWL, and CL designed the virtual screening strategy. BCZ built the homology model and analyzed the structure-activity relationship. SWL, SFH and YK constructed the ROCS-BART model and deployed PIGNet2. YSJ calculated the virtual screening scores. YSJ and BCZ analyzed the virtual screening results and selected the candidate compounds. ZBG and YZ designed wet-lab experiments. YZ, HCW and AYW conducted FDSS/μCell screening and whole-cell patch clamp recording. YSJ, SFH and YZ wrote the manuscript. All authors reviewed the manuscript before submission.

Corresponding authors

Correspondence to Zhao-bing Gao or Yue Kong.

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Ji, Ys., Zeng, Y., Hu, Sf. et al. AI-enhanced virtual screening approach to hit identification for GluN1/GluN3A NMDA receptor. Acta Pharmacol Sin 47, 41–52 (2026). https://doi.org/10.1038/s41401-025-01644-1

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