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

Journal: Acta pharmacologica Sinica
Published Date: Aug 12, 2025

Abstract

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 µM) in experimental validation. The most potent hit, with an IC of 4.16 ± 0.65 µM, 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.

Authors

  • Li Han
    Hubei Provincial Institute for Food Supervision and Test, Hubei Provincial Engineering and Technology Research Center for Food Quality and Safety Test, Wuhan 430075, China.
  • Yue Zeng
    State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
  • Zhi-Yan Qu
    State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
  • Sui Fang
    State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
  • Hai-Ying Wang
    School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China.
  • Ya-Shuo Dong
    State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
  • Xiang-Ming Zeng
    Software and Big Data Technology Department, Dalian Neusoft University of Information, Dalian, 116023, China.
  • Tong-Yan Zhang
    Software and Big Data Technology Department, Dalian Neusoft University of Information, Dalian, 116023, China.
  • Ze-Bin Yu
    Software and Big Data Technology Department, Dalian Neusoft University of Information, Dalian, 116023, China.
  • Ling Kang
    Neusoft Research Institute, Dalian Neusoft University of Information, Dalian, 116023, China. kangling@neusoft.edu.cn.
  • Zhao-Bing Gao
    State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China. zbgao@simm.ac.cn.
  • Quan Guo

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