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

文献类型：期刊论文

作者Li, Shi-wei7,8; Zeng, Yue4,5,6; Wu, Sa-nan7,8; Ma, Xin-yue3; Xu, Chao7,8; Li, Zong-quan3; Fang, Sui6; Chen, Xue-qin6; Gao, Zhao-bing2,5,6; Bai, Fang1,3,7,8
刊名ACTA PHARMACOLOGICA SINICA
出版日期2025-07-14
页码10
关键词N-methyl-D-aspartate receptors GluN1/GluN3A deep learning molecular docking virtual screening binding site identification
ISSN号1671-4083
DOI10.1038/s41401-025-01607-6
通讯作者Gao, Zhao-bing(zbgao@simm.ac.cn) ; Bai, Fang(baifang@shanghaitech.edu.cn)
英文摘要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 mu M) 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.
WOS关键词EXCITATORY GLYCINE RECEPTORS ; ALLOSTERIC MODULATION ; SYNAPTIC PLASTICITY ; SPINE DENSITY ; SUBUNIT ; EXPRESSION ; PREDICTION ; PROTEIN
资助项目Shanghai Science and Technology Development Funds[24JS2850200] ; Shanghai Science and Technology Development Funds[24JS2850100] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB0830403] ; Strategic Priority Research Program of the Chinese Academy of Sciences[SHTAI4S202404] ; National Key R&D Program of China[2022YFC3400501] ; National Key R&D Program of China[2022YFC3400500] ; Shanghai Frontiers Science Center for Biomacromolecules and Precision Medicine at ShanghaiTech University[2021ZD0200900]
WOS研究方向Chemistry ; Pharmacology & Pharmacy
语种英语
WOS记录号WOS:001529740800001
出版者NATURE PUBL GROUP
源URL[http://119.78.100.183/handle/2S10ELR8/318840]  
专题中国科学院上海药物研究所
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.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China
4.Fudan Univ, Sch Pharm, Dept Pharmacol, Shanghai 200032, Peoples R China
5.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
6.Chinese Acad Sci, Shanghai Inst Mat Med, State Key Lab Drug Res, Shanghai 201203, Peoples R China
7.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai 201210, Peoples R China
8.ShanghaiTech Univ, Shanghai Inst Adv Immunochem Studies, Shanghai 201210, Peoples R China
推荐引用方式
GB/T 7714		 Li, Shi-wei,Zeng, Yue,Wu, Sa-nan,et al. Discovery of selective GluN1/GluN3A NMDA receptor inhibitors using integrated AI and physics-based approaches[J]. ACTA PHARMACOLOGICA SINICA,2025:10.
APA Li, Shi-wei.,Zeng, Yue.,Wu, Sa-nan.,Ma, Xin-yue.,Xu, Chao.,...&Bai, Fang.(2025).Discovery of selective GluN1/GluN3A NMDA receptor inhibitors using integrated AI and physics-based approaches.ACTA PHARMACOLOGICA SINICA,10.
MLA Li, Shi-wei,et al."Discovery of selective GluN1/GluN3A NMDA receptor inhibitors using integrated AI and physics-based approaches".ACTA PHARMACOLOGICA SINICA (2025):10.

入库方式： OAI收割

来源：上海药物研究所

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