Discovery of novel GluN1/GluN3A NMDA receptor inhibitors using a deep learning-based method
文献类型：期刊论文
| 作者 | Wang, Shi-hang6,7; Zeng, Yue3,4,5; Yang, Hao6,7; Tian, Si-yuan6,7; Zhou, Yong-qi6,7; Wang, Lin6,7; Chen, Xue-qin5; Wang, Hai-ying5,6; Gao, Zhao-bing2,4,5 ; Bai, Fang1,6,7,8
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| 刊名 | ACTA PHARMACOLOGICA SINICA
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| 出版日期 | 2025-05-12 |
| 页码 | 8 |
| 关键词 | |
| ISSN号 | 1671-4083 |
| DOI | 10.1038/s41401-025-01571-1 |
| 英文摘要 | 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 mu M for GluN1/GluN3A. Further optimization is required to enhance its selectivity, as it exhibited IC50 values of 3.89 +/- 0.79 mu M for GluN1/GluN2A and 1.03 +/- 0.21 mu M 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. |
| WOS关键词 | LIGAND-BINDING DOMAIN ; ALLOSTERIC MODULATION ; SYNAPTIC PLASTICITY ; GLYCINE RECEPTORS ; SUBUNIT ; EXPRESSION |
| 资助项目 | Shanghai Science and Technology Development Funds[24JS2850100] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB0830403] ; Shanghai Tech AI4S Initiative[SHTAI4S202404] ; National Key R&D Program of China[2022YFC3400501] ; National Key R&D Program of China[2022YFC3400500] ; Start-up package from Shanghai Tech University ; Shanghai Frontiers Science Center for Biomacromolecules and Precision Medicine at Shanghai Tech University ; National Science and Technology Innovation 2030 Major Program[2021ZD0200900] ; [24JS2850200] |
| WOS研究方向 | Chemistry ; Pharmacology & Pharmacy |
| 语种 | 英语 |
| WOS记录号 | WOS:001485559900001 |
| 出版者 | NATURE PUBL GROUP |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/317887]  |
| 专题 | 新药研究国家重点实验室 |
| 通讯作者 | Gao, Zhao-bing; Bai, Fang |
| 作者单位 | 1.Shanghai Clin Res & Trial Ctr, Shanghai 201210, Peoples R China 2.Chinese Acad Sci, Zhongshan Inst Drug Discovery, Shanghai Inst Mat Med, Zhongshan 528400, Peoples R China 3.Fudan Univ, Sch Pharm, Dept Pharmacol, Shanghai 200032, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 5.Chinese Acad Sci, Shanghai Inst Mat Med, State Key Lab Drug Res, Shanghai 201203, Peoples R China 6.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai 201210, Peoples R China 7.ShanghaiTech Univ, Shanghai Inst Adv Immunochem Studies, Shanghai 201210, Peoples R China 8.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Shi-hang,Zeng, Yue,Yang, Hao,et al. Discovery of novel GluN1/GluN3A NMDA receptor inhibitors using a deep learning-based method[J]. ACTA PHARMACOLOGICA SINICA,2025:8. |
| APA | Wang, Shi-hang.,Zeng, Yue.,Yang, Hao.,Tian, Si-yuan.,Zhou, Yong-qi.,...&Bai, Fang.(2025).Discovery of novel GluN1/GluN3A NMDA receptor inhibitors using a deep learning-based method.ACTA PHARMACOLOGICA SINICA,8. |
| MLA | Wang, Shi-hang,et al."Discovery of novel GluN1/GluN3A NMDA receptor inhibitors using a deep learning-based method".ACTA PHARMACOLOGICA SINICA (2025):8. |
入库方式： OAI收割
来源：上海药物研究所
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