AI-enhanced virtual screening approach to hit identification for GluN1/GluN3A NMDA receptor
文献类型：期刊论文
| 作者 | Ji, Yue-shan2; Zeng, Yue3,4,5; Hu, Shao-fei2; Li, Shu-wang2; Zhang, Bei-chen2; Liu, Chang2; Wu, Hao-chen3; Wang, An-yang3; Gao, Zhao-bing1,3,4; Kong, Yue2 |
| 刊名 | ACTA PHARMACOLOGICA SINICA
 |
| 出版日期 | 2025-08-26 |
| 页码 | 12 |
| 关键词 | |
| ISSN号 | 1671-4083 |
| DOI | 10.1038/s41401-025-01644-1 |
| 英文摘要 | N-methyl-D-aspartate receptors (NMDARs) are calcium-permeable ionotropic glutamate receptors broadly expressed throughout the central nervous system, where they play crucial roles in neuronal development and synaptic plasticity. Among the various subtypes, the GluN1/GluN3A receptor represents a unique glycine-gated NMDAR with notably low calcium permeability. Despite its distinctive properties, GluN1/GluN3A remains understudied, particularly with respect to pharmacological tools development. This scarcity poses challenges for deeper investigation into its physiological functions and therapeutic relevance. In this study, we employed a hybrid virtual screening (VS) pipeline that integrates ligand-based and structure-based approaches for the efficient and precise identification of small-molecule candidates targeting GluN1/GluN3A. A large compound library comprising 18 million molecules was screened using an AI-enhanced multi-stage method. The initial phase utilized shape similarity ranking via ROCS-BART, followed by refinement with a graph neural network (GNN)-based drug-target interaction model to enhance docking accuracy. Functional validation using calcium flux (FDSS/mu Cell) identified two compounds with IC50 values below 10 mu M. Of these, one candidate exhibited potent inhibitory activity with an IC50 of 5.31 +/- 1.65 mu M, which was further confirmed through manual patch-clamp recordings. These findings highlight an AI-enhanced VS workflow that achieves both efficiency and precision, providing a promising framework for exploring elusive targets such as GluN1/GluN3A. |
| WOS关键词 | POSITIVE ALLOSTERIC MODULATORS ; SYNAPTIC PLASTICITY ; GLYCINE RECEPTORS ; FORCE-FIELD ; SUBUNIT ; EXPRESSION ; ANTAGONISTS ; DISCOVERY ; DOCKING ; BINDING |
| 资助项目 | Strategic Priority Research Program of the Chinese Academy of Sciences[XDB0830403] ; Strategic Priority Research Program of the Chinese Academy of Sciences[2021ZD0200900] |
| WOS研究方向 | Chemistry ; Pharmacology & Pharmacy |
| 语种 | 英语 |
| WOS记录号 | WOS:001556811300001 |
| 出版者 | NATURE PUBL GROUP |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/321324]  |
| 专题 | 国家级研究中心_原创新药研究全国重点实验室 |
| 通讯作者 | Gao, Zhao-bing; Kong, Yue |
| 作者单位 | 1.Chinese Acad Sci, Zhongshan Inst Drug Discovery, Shanghai Inst Mat Med, Zhongshan 528400, Peoples R China 2.Lepu Med Technol Beijing Co Ltd, Beijing 102200, Peoples R China 3.Chinese Acad Sci, Shanghai Inst Mat Med, State Key Lab Drug Res, Shanghai 201203, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 5.Fudan Univ, Sch Pharm, Dept Pharmacol, Shanghai 200032, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ji, Yue-shan,Zeng, Yue,Hu, Shao-fei,et al. AI-enhanced virtual screening approach to hit identification for GluN1/GluN3A NMDA receptor[J]. ACTA PHARMACOLOGICA SINICA,2025:12. |
| APA | Ji, Yue-shan.,Zeng, Yue.,Hu, Shao-fei.,Li, Shu-wang.,Zhang, Bei-chen.,...&Kong, Yue.(2025).AI-enhanced virtual screening approach to hit identification for GluN1/GluN3A NMDA receptor.ACTA PHARMACOLOGICA SINICA,12. |
| MLA | Ji, Yue-shan,et al."AI-enhanced virtual screening approach to hit identification for GluN1/GluN3A NMDA receptor".ACTA PHARMACOLOGICA SINICA (2025):12. |
入库方式： OAI收割
来源：上海药物研究所
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