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Identification of small-molecule inhibitors for GluN1/GluN3A NMDA receptors via a multiscale CNN-based prediction model

文献类型：期刊论文

作者Han, Li2; Zeng, Yue1,3,4; Qu, Zhi-yan1,3; Fang, Sui1; Wang, Hai-ying1,5; Dong, Ya-shuo1; Zeng, Xiang-ming2; Zhang, Tong-yan2; Yu, Ze-bin2; Kang, Ling6
刊名ACTA PHARMACOLOGICA SINICA
出版日期2025-08-12
页码9
关键词N-methyl-D-aspartate receptors GluN1/GluN3A drug-target binding affinity convolutional neural networks ImageDTA virtual screening
ISSN号1671-4083
DOI10.1038/s41401-025-01630-7
英文摘要N-methyl-D-aspartate receptors (NMDARs) are critical mediators of excitatory neurotransmission and are composed of seven subunits (GluN1, GluN2A-D, and GluN3A-B) that form diverse receptor subtypes. While GluN1/GluN2 subtypes have been extensively characterized and have led to approved therapeutics, the GluN1/GluN3A subtype remains underexplored despite emerging evidence of its involvement in neuropsychiatric disorders. Efficient identification of modulators requires accurate prediction of drug-target affinity (DTA), particularly for challenging targets such as GluN1/GluN3A. In this study, we applied the ImageDTA model, which is a multiscale 2D convolutional neural network (CNN), to virtually screen 18 million small molecules for GluN1/GluN3A inhibitors. This artificial intelligence (AI)-driven approach prioritized 12 compounds, three of which demonstrated potent inhibitory activity (IC50 < 30 M) in experimental validation. The most potent hit, with an IC50 of 4.16 +/- 0.65 mu M, revealed a novel structural scaffold, thus highlighting the potential of AI in accelerating drug discovery for underexplored receptor subtypes. These findings establish a robust framework for advancing GluN1/GluN3A-targeted therapeutics.
资助项目Dalian Science and Technology Innovation Fund Program[2022JJ12GX017] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB0830403] ; United Foundation for Medico-engineering Cooperation from Dalian Neusoft University of Information and the Second Hospital of Dalian Medical University[LH-JSRZ-202201] ; Technology Innovation Project of Dalian Neusoft University of Information[TIFP202302] ; Technology Innovation Project of Dalian Neusoft University of Information[2021ZD0200900] ; Neusoft Research Institute of Dalian Neusoft University of Information
WOS研究方向Chemistry ; Pharmacology & Pharmacy
语种英语
WOS记录号WOS:001549477200001
出版者NATURE PUBL GROUP
源URL[http://119.78.100.183/handle/2S10ELR8/321228]  
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Kang, Ling; Gao, Zhao-bing; Guo, Quan
作者单位1.Chinese Acad Sci, Shanghai Inst Mat Med, State Key Lab Drug Res, Shanghai 201203, Peoples R China
2.Dalian Neusoft Univ Informat, Software & Big Data Technol Dept, Dalian 116023, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Fudan Univ, Sch Pharm, Dept Pharmacol, Shanghai 200032, Peoples R China
5.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai 201210, Peoples R China
6.Dalian Neusoft Univ Informat, Neusoft Res Inst, Dalian 116023, Peoples R China
7.Chinese Acad Sci, Zhongshan Inst Drug Discovery, Shanghai Inst Mat Med, Zhongshan 528400, Peoples R China
推荐引用方式
GB/T 7714		 Han, Li,Zeng, Yue,Qu, Zhi-yan,et al. Identification of small-molecule inhibitors for GluN1/GluN3A NMDA receptors via a multiscale CNN-based prediction model[J]. ACTA PHARMACOLOGICA SINICA,2025:9.
APA Han, Li.,Zeng, Yue.,Qu, Zhi-yan.,Fang, Sui.,Wang, Hai-ying.,...&Guo, Quan.(2025).Identification of small-molecule inhibitors for GluN1/GluN3A NMDA receptors via a multiscale CNN-based prediction model.ACTA PHARMACOLOGICA SINICA,9.
MLA Han, Li,et al."Identification of small-molecule inhibitors for GluN1/GluN3A NMDA receptors via a multiscale CNN-based prediction model".ACTA PHARMACOLOGICA SINICA (2025):9.

入库方式： OAI收割

来源：上海药物研究所

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