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Contrastive learning-based drug screening model for GluN1/GluN3A inhibitors

Acta Pharmacologica Sinica (2025)Cite this article

Abstract

GluN3A-containing NMDA receptors have recently emerged as promising therapeutic targets for neurological disorders. However, discovering potent modulators remains a significant challenge, primarily due to the limitations of traditional high-throughput screening methods. In this study, we introduce a novel drug-target affinity prediction method, CLG-DTA, designed to enhance drug discovery for the GluN1/GluN3A receptor. This graph contrastive learning-based method incorporates natural language supervision by transforming regression labels into textual representation, and integrating them with traditional affinity data to enhance molecular representation. Additionally, a numerical knowledge graph is employed to refine continuous text embeddings, enabling precise modeling of complex drug-target interactions across diverse data modalities. Using CLG-DTA, we screened a library of 18 million compounds and identified 12 candidates for experimental validation. Among them, five compounds exhibited significant activity, with Boeravinone E demonstrating the highest potency (\({{{\rm{IC}}}}_{50}\) = 3.40 \(\pm\) 0.91 μM). These findings highlight the potential of CLG-DTA in accelerating the identification of promising GluN1/GluN3A modulators and lay a robust foundation for future therapeutic development.

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Fig. 1: The workflow of the CLG-DTA model for target-based drug discovery.
Fig. 2: Contrastive learning pre-training framework.
Fig. 3: Pre-training phase with CN-KG constraint and fine-tuning.
Fig. 4: Visualization of MSE and PCC results on different datasets across four test experimental scenarios.
Fig. 5: Effects of Boeravinone E on GluN1/GluN3A receptor.
Fig. 6: Predicted binding of Boeravinone E to the GluN1/GluN3A receptor.

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Acknowledgements

The work was supported in part by the National Key Research and Development Program of China (2023YFC2705700 to WBH), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB0830403 to ZBG), National Science Foundation of China (No. 62476203 to WBH), the Guangdong Provincial Natural Science Foundation General Project (No. 2025A1515012155 to WBH), the National Science and Technology Innovation 2030 Major Program (2021ZD0200900), Open Fund for Research Projects of the Ministry of Education Key Laboratory of Embedded System and Service Computing, Tongji University (No. ESSCKF2024-01), and Key Program of Hubei Natural Science Foundation Traditional Chinese Medicine Innovation and Development Joint Fund (2025AFD470).

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  1. These authors contributed equally: Kun Li, Yue Zeng.

Authors and Affiliations

  1. School of Computer Science, Wuhan University, Wuhan, 430037, China

    Kun Li, Yi-da Xiong, Bo Du & Wen-bin Hu

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

    Yue Zeng, Hao-chen Wu, Sui Fang, Zhi-yan Qu & Zhao-bing Gao

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

    Yue Zeng, Zhi-yan Qu & Zhao-bing Gao

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

    Yue Zeng

  5. Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, 100700, China

    Yan Zhu

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

    Zhao-bing Gao

  7. Shenzhen Research Institute, Wuhan University, Shenzhen, 518057, China

    Wen-bin Hu

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Contributions

WBH and ZBG designed the study. KL, YDX, Yan Z, BD, and WBH developed the computational methods. Yue Z, HCW, and SF performed the activity validation. ZYQ and YDX conducted homology modeling and molecular docking analyses. KL, Yue Z, ZBG, and WBH drafted the original manuscript. KL, Yue Z, YDX, HCW, SF, Yue Z, BD, ZBG, and WBH edited and reviewed the manuscript before submission.

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Correspondence to Yan Zhu, Bo Du, Zhao-bing Gao or Wen-bin Hu.

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Li, K., Zeng, Y., Xiong, Yd. et al. Contrastive learning-based drug screening model for GluN1/GluN3A inhibitors. Acta Pharmacol Sin (2025). https://doi.org/10.1038/s41401-025-01580-0

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