Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

An efficient deep learning-based strategy to screen inhibitors for GluN1/GluN3A receptor

Acta Pharmacologica Sinica (2025)Cite this article

Abstract

The GluN1/GluN3A receptor, a unique excitatory glycine receptor recently identified in the central nervous system, challenges traditional perspectives of N-methyl-D-aspartate (NMDA) receptor diversity and glycinergic signaling. Its role in emotional regulation positions it as a potential therapeutic target for neuropsychiatric disorders. However, pharmacological research on GluN1/GluN3A receptors remains at an early stage. Traditional high-throughput screening methods for ion channel drug discovery often lack efficiency, particularly when applied to large compound libraries. To address this concern, we designed a deep learning-based strategy that balances efficiency and accuracy for identifying GluN1/GluN3A inhibitors. First, a sequence-based scoring function was developed to rapidly screen a library containing 18 million compounds, reducing the pool to approximately 105 candidates. Next, two complex-based scoring functions, IGModel and RTMScore, were employed to precisely score and rank the remaining candidates. Finally, an active molecule with an IC50 of 2.87 ± 0.80 μM for the GluN1/GluN3A receptor was confirmed through whole-cell voltage-clamp electrophysiology. This study also presents a paradigm for integrating deep learning into rapid and precise high-throughput screening.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Overview of the screening strategy.
Fig. 2: Processes for the training and inference of sequence-based models.
Fig. 3: Inference based on compound-based SFs.
Fig. 4: Data distribution in the five-fold cross-validation dataset.
Fig. 5: Performance of the hybrid model on the CASF2016 screening power test set with different RTMScore weights.
Fig. 6: Visualization of the GluN1/GluN3A receptor.
Fig. 7: Evaluation of PAT-505 on distinct NMDA receptor subtypes.

Similar content being viewed by others

References

  1. Hansen KB, Yi F, Perszyk RE, Furukawa H, Wollmuth LP, Gibb AJ, et al. Structure, function, and allosteric modulation of NMDA receptors. J Gen Physiol. 2018;150:1081–105.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. Traynelis SF, Wollmuth LP, McBain CJ, Menniti FS, Vance KM, Ogden KK, et al. Glutamate receptor ion channels: structure, regulation, and function. Pharmacol Rev. 2010;62:405–96.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Newcomer JW, Farber NB, Olney JW. NMDA receptor function, memory, and brain aging. Dialogues Clin Neurosci. 2000;2:219–32.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Grand T, Abi Gerges S, David M, Diana MA, Paoletti P. Unmasking GluN1/GluN3A excitatory glycine NMDA receptors. Nat Commun. 2018;9:4769.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Galliano E, Schonewille M, Peter S, Rutteman M, Houtman S, Jaarsma D, et al. Impact of NMDA receptor overexpression on cerebellar Purkinje cell activity and motor learning. eNeuro. 2018;5:ENEURO.0270-17.2018.

  6. Karakas E, Furukawa H. Crystal structure ofa heterotetrameric NMDA receptor ion channel. Science. 2014;344:992–7.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Ma T, Cheng Q, Chen C, Luo Z, Feng D. Excessive activation of NMDA receptors in the pathogenesis of multiple peripheral organs via mitochondrial dysfunction, oxidative stress, and inflammation. SN Compr Clin Med. 2020;2:551–69.

    Article  CAS  Google Scholar 

  8. Parsons CG, Danysz W, Quack G. Memantine is a clinically well tolerated N-methyl-D-aspartate (NMDA) receptor antagonist—a review of preclinical data. Neuropharmacology. 1999;38:735–67.

    Article  PubMed  CAS  Google Scholar 

  9. Chatterton JE, Awobuluyi M, Premkumar LS, Takahashi H, Talantova M, Shin Y, et al. Excitatory glycine receptors containing the NR3 family of NMDA receptor subunits. Nature. 2002;415:793–8.

    Article  PubMed  CAS  Google Scholar 

  10. Bossi S, Pizzamiglio L, Paoletti P. Excitatory GluN1/GluN3A glycine receptors (eGlyRs) in brain signaling. Trends Neurosci. 2023;46:667–81.

    Article  PubMed  CAS  Google Scholar 

  11. Zeng Y, Zheng Y, Zhang T, Ye F, Zhan L, Kou Z, et al. Identification of a subtype-selective allosteric inhibitor of GluN1/GluN3 NMDA receptors. Front Pharmacol. 2022; 13.

  12. Wang W, Gao X. Deep learning in bioinformatics. Methods 2019;166:1–3.

    Article  PubMed  Google Scholar 

  13. Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov. 2019;18:463–77.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Lavecchia A. Deep learning in drug discovery: opportunities, challenges and future prospects. Drug Discov Today. 2019;24:2017–32.

    Article  PubMed  Google Scholar 

  15. Lo YC, Rensi SE, Torng W, Altman RB. Machine learning in chemoinformatics and drug discovery. Drug Discov Today. 2018;23:1538–46.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Carpenter KA, Cohen DS, Jarrell JT, Huang X. Deep learning and virtual drug screening. Future Med Chem. 2018;10:2557–67.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Oliveira T, Silva M, Maia E, Silva A, Taranto A. Virtual screening algorithms in drug discovery: a review focused on machine and deep learning methods. Drugs Drug Candidates. 2023;2:311–34.

    Article  Google Scholar 

  18. Koh HY, Nguyen ATN, Pan S, May LT, Webb GI. Physicochemical graph neural network for learning protein–ligand interaction fingerprints from sequence data. Nat Mach Intell. 2024;6:673–87.

    Article  Google Scholar 

  19. Ozturk H, Ozgur A, Ozkirimli E. DeepDTA: deep drug-target binding affinity prediction. Bioinformatics. 2018;34:i821–i9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Nguyen T, Le H, Quinn TP, Nguyen T, Le TD, Venkatesh S. GraphDTA: predicting drug-target binding affinity with graph neural networks. Bioinformatics. 2021;37:1140–7.

    Article  PubMed  CAS  Google Scholar 

  21. Bai P, Miljković F, John B, Lu H. Interpretable bilinear attention network with ___domain adaptation improves drug–target prediction. Nat Mach Intell. 2023;5:126–36.

    Article  Google Scholar 

  22. Lu W, Wu Q, Zhang J, Rao J, Li C, Zheng S. Tankbind: Trigonometry-aware neural networks for drug-protein binding structure prediction. Adv Neural Inf Process Syst. 2022;35:7236–49.

    Google Scholar 

  23. Zheng L, Fan J, Mu Y. OnionNet: a multiple-layer intermolecular-contact-based convolutional neural network for protein-ligand binding affinity prediction. ACS Omega. 2019;4:15956–65.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Wang Z, Zheng L, Liu Y, Qu Y, Li YQ, Zhao M, et al. OnionNet-2: A convolutional neural network model for predicting protein-ligand binding affinity based on residue-atom contacting shells. Front Chem. 2021;9:753002.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Nguyen DD, Wei GW. AGL-score: algebraic graph learning score for protein-ligand binding scoring, ranking, docking, and screening. J Chem Inf Model. 2019;59:3291–304.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Wang Z, Wang S, Li Y, Guo J, Wei Y, Mu Y, et al. A new paradigm for applying deep learning to protein-ligand interaction prediction. Brief Bioinform. 2024;25:bbae145.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Wang Z, Zheng L, Wang S, Lin M, Wang Z, Kong AW, et al. A fully differentiable ligand pose optimization framework guided by deep learning and a traditional scoring function. Brief Bioinform. 2023;24:bbac520.

    Article  PubMed  Google Scholar 

  28. Zheng L, Meng J, Jiang K, Lan H, Wang Z, Lin M, et al. Improving protein-ligand docking and screening accuracies by incorporating a scoring function correction term. Brief Bioinform. 2022;23:bbac051.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Shen T, Liu F, Wang Z, Sun J, Bu Y, Meng J, et al. zPoseScore model for accurate and robust protein-ligand docking pose scoring in CASP15. Proteins. 2023;91:1837–49.

    Article  PubMed  CAS  Google Scholar 

  30. Méndez-Lucio O, Ahmad M, del Rio-Chanona EA, Wegner JK. A geometric deep learning approach to predict binding conformations of bioactive molecules. Nat Mach Intell. 2021;3:1033–9.

    Article  Google Scholar 

  31. Shen C, Zhang X, Deng Y, Gao J, Wang D, Xu L, et al. Boosting protein-ligand binding pose prediction and virtual screening based on residue-atom distance likelihood potential and graph transformer. J Med Chem. 2022;65:10691–706.

    Article  PubMed  CAS  Google Scholar 

  32. Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596:583–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Lin Z, Akin H, Rao R, Hie B, Zhu Z, Lu W, et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science. 2023;379:1123–30.

    Article  PubMed  CAS  Google Scholar 

  34. Edwards C, Lai T, Ros K, Honke G, Cho K, Ji H. Translation between molecules and natural language. Preprint at https://arxiv.org/abs/2204.11817.

  35. Liu T, Lin Y, Wen X, Jorissen RN, Gilson MK. BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res. 2007;35:D198–201.

    Article  PubMed  CAS  Google Scholar 

  36. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinforma. 2009;10:421.

    Article  Google Scholar 

  37. Yan X, Lu Y, Li Z, Wei Q, Gao X, Wang S, et al. PointSite: A point cloud segmentation tool for identification of protein ligand binding atoms. J Chem Inf Model. 2022;62:2835–45.

    Article  PubMed  CAS  Google Scholar 

  38. Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31:455–61.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Elnaggar A, Heinzinger M, Dallago C, Rehawi G, Wang Y, Jones L, et al. ProtTrans: Toward understanding the language of life through self-supervised learning. IEEE Trans Pattern Anal Mach Intell. 2022;44:7112–27.

    Article  PubMed  Google Scholar 

  40. Pei Q, Wu L, Zhu J, Xia Y, Xie S, Qin T, et al. Breaking the barriers of data scarcity in drug-target affinity prediction. Brief Bioinform. 2023;24:bbad386.

    Article  PubMed  Google Scholar 

  41. Meda RS, Farimani AB. BAPULM: Binding affinity prediction using language models. Preprint at https://arxiv.org/abs/2411.04150.

  42. Su M, Yang Q, Du Y, Feng G, Liu Z, Li Y, et al. Comparative assessment of scoring functions: The CASF-2016 Update. J Chem Inf Model. 2019;59:895–913.

    Article  PubMed  CAS  Google Scholar 

  43. Zhu Z, Yi F, Epplin MP, Liu D, Summer SL, Mizu R, et al. Negative allosteric modulation of GluN1/GluN3 NMDA receptors. Neuropharmacology. 2020;176:108117.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Crawley O, Conde-Dusman MJ, Perez-Otano I. GluN3A NMDA receptor subunits: more enigmatic than ever? J Physiol. 2022;600:261–76.

    Article  PubMed  CAS  Google Scholar 

  45. Abramson J, Adler J, Dunger J, Evans R, Green T, Pritzel A, et al. Accurate structure prediction of biomolecular interactions with AlphaFold3. Nature. 2024;630:493–500.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Acknowledgements

This research is supported by the Taishan Scholars Program of Shandong Province (tstp20240807), Shandong Provincial Natural Science Foundation (ZR2024MA071) and the National Science and Technology Innovation 2030 Major Program (2021ZD0200900). The authors sincerely thank the support from the Core Facility Sharing Platform of Shandong University and the National Demonstration Center for Experimental Physics Education (Shandong University).

Author information

Author notes

  1. These authors contributed equally: Ze-chen Wang, Yue Zeng, Jin-yuan Sun

Authors and Affiliations

  1. School of Physics, Shandong University, Jinan, 250100, China

    Ze-chen Wang, Yang-yang Li & Wei-feng Li

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

    Yue Zeng, Xue-qin Chen, Hao-chen Wu & Zhao-bing Gao

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

    Yue Zeng, Jin-yuan Sun & Zhao-bing Gao

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

    Yue Zeng

  5. School of Biological Science, Nanyang Technological University, Singapore, 637551, Singapore

    Yu-guang Mu

  6. Shenzhen Zelixir Biotech Co. Ltd, Hengtaiyu Park, Shenzhen, 518107, China

    Liang-zhen Zheng

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

    Zhao-bing Gao

Authors
  1. Ze-chen Wang
    View author publications

    Search author on:PubMed Google Scholar

  2. Yue Zeng
    View author publications

    Search author on:PubMed Google Scholar

  3. Jin-yuan Sun
    View author publications

    Search author on:PubMed Google Scholar

  4. Xue-qin Chen
    View author publications

    Search author on:PubMed Google Scholar

  5. Hao-chen Wu
    View author publications

    Search author on:PubMed Google Scholar

  6. Yang-yang Li
    View author publications

    Search author on:PubMed Google Scholar

  7. Yu-guang Mu
    View author publications

    Search author on:PubMed Google Scholar

  8. Liang-zhen Zheng
    View author publications

    Search author on:PubMed Google Scholar

  9. Zhao-bing Gao
    View author publications

    Search author on:PubMed Google Scholar

  10. Wei-feng Li
    View author publications

    Search author on:PubMed Google Scholar

Contributions

WFL and ZBG designed the study. ZCW and JYS designed the deep learning-based screening strategy and conducted high-throughput virtual screening. YZ, XQC, and HCW designed and conducted wet-lab experiments. YYL, YGM, and LZZ analyzed the data. ZCW and YZ wrote the manuscript. All authors reviewed the manuscript before submission.

Corresponding authors

Correspondence to Zhao-bing Gao or Wei-feng Li.

Ethics declarations

Competing interests

The authors declare no competing interests.

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Zc., Zeng, Y., Sun, Jy. et al. An efficient deep learning-based strategy to screen inhibitors for GluN1/GluN3A receptor. Acta Pharmacol Sin (2025). https://doi.org/10.1038/s41401-025-01513-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41401-025-01513-x

Keywords

Search

Advanced search

Quick links