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Discovery of selective GluN1/GluN3A NMDA receptor inhibitors using integrated AI and physics-based approaches

Acta Pharmacologica Sinica volume 47, pages 22–31 (2026)Cite this article

Abstract

N-methyl-D-aspartate receptors (NMDARs) are glutamate-gated ion channels essential for synaptic transmission and plasticity in the central nervous system. GluN1/GluN3A, an unconventional NMDAR subtype functioning as an excitatory glycine receptor, has been implicated in mood regulation, with high expression in brain regions governing emotional and motivational states. However, therapeutic exploration has been significantly hindered by a lack of potent and selective modulators, limited structural data and the intrinsic complexity of ion channels. Here, we introduce a compound virtual screening pipeline that combines artificial intelligence and physical models, integrating two sequence-based deep learning prediction models (TEFDTA and ESMLigSite) with a molecular docking approach. This approach was employed to identify potential inhibitors against GluN1/GluN3A by screening a commercial database containing 18 million compounds. The strategy resulted in an impressive hit rate of 50% for discovering inhibitors, with the most promising compound exhibiting strong inhibitory activity (IC50 = 1.26 ± 0.23 μM) and remarkable target specificity (>23-fold selectivity over the GluN1/GluN2A receptor). These findings highlight the effectiveness of AI-assisted strategies in addressing challenges related to unconventional ion channels and pave the way for new therapeutic exploration.

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Fig. 1: Workflow for screening inhibitor candidates targeting GluN1/GluN3A NMDAR.
Fig. 2: Two-step screening results and predicted interaction modes for the representative inhibitor ligand.
Fig. 3: Evaluation of the inhibition of 12 candidate compounds against the GluN1/GluN3A receptor using FDSS/μCell based on calcium fluorescence signal changes.
Fig. 4: Evaluation of six hits via whole-cell patch-clamp recordings.

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Acknowledgements

This work was supported by Shanghai Science and Technology Development Funds (Grant IDs: 24JS2850200 and 24JS2850100), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant ID: XDB0830403), ShanghaiTech AI4S Initiative SHTAI4S202404, National Key R&D Program of China (Grant IDs: 2022YFC3400501 & 2022YFC3400500), start-up package from ShanghaiTech University, and Shanghai Frontiers Science Center for Biomacromolecules and Precision Medicine at ShanghaiTech University, and the National Science and Technology Innovation 2030 Major Program (Grant ID: 2021ZD0200900). The authors appreciate the technical support provided by the high-performance computing cluster of ShanghaiTech University.

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Author notes

  1. These authors contributed equally: Shi-wei Li, Yue Zeng, Sa-nan Wu, Xin-yue Ma, Chao Xu, Zong-quan Li

Authors and Affiliations

  1. Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China

    Shi-wei Li, Sa-nan Wu, Chao Xu & Fang Bai

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

    Yue Zeng, Sui Fang, Xue-qin Chen & 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. School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China

    Xin-yue Ma, Zong-quan Li & Fang Bai

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

    Zhao-bing Gao

  7. Shanghai Clinical Research and Trial Center, Shanghai, 201210, China

    Fang Bai

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Contributions

FB and ZBG designed the study. FB and SWL proposed the screening pipeline, SNW, CX and XYM collected corresponding data for the screening, ZQL conducted initial virtual screening, SWL, SNW and CX conducted fine screening. YZ, SF, and XQC designed and conducted wet-lab experiments. SWL and YZ wrote the manuscript. All authors reviewed the manuscript before submission.

Corresponding authors

Correspondence to Zhao-bing Gao or Fang Bai.

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Li, Sw., Zeng, Y., Wu, Sn. et al. Discovery of selective GluN1/GluN3A NMDA receptor inhibitors using integrated AI and physics-based approaches. Acta Pharmacol Sin 47, 22–31 (2026). https://doi.org/10.1038/s41401-025-01607-6

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