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Discovery of novel GluN1/GluN3A NMDA receptor inhibitors using a deep learning-based method

Acta Pharmacologica Sinica (2025)Cite this article

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 IC50 of 0.98 ± 0.13 μM for GluN1/GluN3A. Further optimization is required to enhance its selectivity, as it exhibited IC50 values of 3.89 ± 0.79 μM for GluN1/GluN2A and 1.03 ± 0.21 μM for GluN1/GluN3B. This work highlights the potential of AI-driven molecular representation technologies to facilitate scaffold hopping and enhance similarity-based virtual screening for drug discovery.

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Fig. 1: Schematic representation of the screening process for GluN1/GluN3A inhibitors.
Fig. 2: Profiles of known inhibitors targeting distinct binding sites on GluN1/GluN3A receptor.
Fig. 3: Evaluation of GM-10’s activity and selectivity via whole-cell patch-clamp recordings.
Fig. 4: 3D and 2D similarity comparison of GM-10 with WZB117.

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Acknowledgements

This work was supported by Shanghai Science and Technology Development Funds (Grant IDs: 24JS2850100 and 24JS2850200), 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.

Author information

Author notes

  1. These authors contributed equally: Shi-hang Wang, Yue Zeng, Hao Yang, Si-yuan Tian, Yong-qi Zhou, Lin Wang

Authors and Affiliations

  1. Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, Shanghai, 201210, China

    Shi-hang Wang, Hao Yang, Si-yuan Tian, Yong-qi Zhou, Lin Wang & Fang Bai

  2. School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China

    Shi-hang Wang, Hao Yang, Si-yuan Tian, Yong-qi Zhou, Lin Wang, Hai-ying Wang & Fang Bai

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

    Yue Zeng, Xue-qin Chen, Hai-ying Wang & Zhao-bing Gao

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

    Yue Zeng & Zhao-bing Gao

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

    Yue Zeng

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

    Zhao-bing Gao

  7. School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China

    Fang Bai

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

    Fang Bai

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Contributions

FB and ZBG contributed to conceptualization. SHW and LW contributed to model development. SHW, HY, YQZ and SYT contributed to data curation. YZ, XQC and HYW designed and conducted wet-lab experiments. SHW, YZ, HY, YQZ, SYT, LW, ZBG, and FB contributed to original draft writing. All authors reviewed the manuscript before submission.

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Correspondence to Zhao-bing Gao or Fang Bai.

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Wang, Sh., Zeng, Y., Yang, H. et al. Discovery of novel GluN1/GluN3A NMDA receptor inhibitors using a deep learning-based method. Acta Pharmacol Sin (2025). https://doi.org/10.1038/s41401-025-01571-1

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