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<p><i>N</i>-methyl-<i>D</i>-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 &lt; 30 µM) in experimental validation. The most potent hit, with an IC<sub>50</sub> 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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年8月12日

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  • Li Han,
  • Yue Zeng,
  • Zhi-yan Qu,
  • Sui Fang,
  • Hai-ying Wang,
  • Ya-shuo Dong,
  • Xiang-ming Zeng,
  • Tong-yan Zhang,
  • Ze-bin Yu,
  • Ling Kang,
  • Zhao-bing Gao,
  • Quan Guo

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 µM) in experimental validation. The most potent hit, with an IC50 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.