An efficient deep learning-based strategy to screen inhibitors for GluN1/GluN3A receptor
文献类型：期刊论文
| 作者 | Wang, Ze-chen3; Zeng, Yue1,2,7; Sun, Jin-yuan1; Chen, Xue-qin2; Wu, Hao-chen2; Li, Yang-yang3; Mu, Yu-guang6; Zheng, Liang-zhen5; Gao, Zhao-bing1,2,4 ; Li, Wei-feng3
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| 刊名 | ACTA PHARMACOLOGICA SINICA
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| 出版日期 | 2025-03-11 |
| 页码 | 9 |
| 关键词 | deep learning
scoring function
GluN1/GluN3A receptor
|
| ISSN号 | 1671-4083 |
| DOI | 10.1038/s41401-025-01513-x |
| 英文摘要 | 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 mu M 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. |
| WOS关键词 | GLYCINE RECEPTORS ; PREDICTION ; LANGUAGE |
| 资助项目 | Taishan Scholars Program of Shandong Province[tstp20240807] ; Shandong Provincial Natural Science Foundation[ZR2024MA071] ; Shandong Provincial Natural Science Foundation[2021ZD0200900] ; Core Facility Sharing Platform of Shandong University ; National Demonstration Center for Experimental Physics Education |
| WOS研究方向 | Chemistry ; Pharmacology & Pharmacy |
| 语种 | 英语 |
| WOS记录号 | WOS:001443791600001 |
| 出版者 | NATURE PUBL GROUP |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/316514]  |
| 专题 | 新药研究国家重点实验室 |
| 通讯作者 | Gao, Zhao-bing; Li, Wei-feng |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Shanghai Inst Mat Med, State Key Lab Drug Res, Shanghai 201203, Peoples R China 3.Shandong Univ, Sch Phys, Jinan 250100, Peoples R China 4.Chinese Acad Sci, Zhongshan Inst Drug Discovery, Shanghai Inst Mat Med, Zhongshan 528400, Peoples R China 5.Shenzhen Zelixir Biotech Co Ltd, Shenzhen 518107, Peoples R China 6.Nanyang Technol Univ, Sch Biol Sci, Singapore 637551, Singapore 7.Fudan Univ, Sch Pharm, Dept Pharmacol, Shanghai 200032, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Ze-chen,Zeng, Yue,Sun, Jin-yuan,et al. An efficient deep learning-based strategy to screen inhibitors for GluN1/GluN3A receptor[J]. ACTA PHARMACOLOGICA SINICA,2025:9. |
| APA | Wang, Ze-chen.,Zeng, Yue.,Sun, Jin-yuan.,Chen, Xue-qin.,Wu, Hao-chen.,...&Li, Wei-feng.(2025).An efficient deep learning-based strategy to screen inhibitors for GluN1/GluN3A receptor.ACTA PHARMACOLOGICA SINICA,9. |
| MLA | Wang, Ze-chen,et al."An efficient deep learning-based strategy to screen inhibitors for GluN1/GluN3A receptor".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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