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<p>The GluN1/GluN3A receptor, a unique excitatory glycine receptor recently identified in the central nervous system, challenges traditional perspectives of <i>N</i>-methyl-<i>D</i>-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 10<sup>5</sup> 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 IC<sub>50</sub> of 2.87 ± 0.80 μM 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.</p>

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An efficient deep learning-based strategy to screen inhibitors for GluN1/GluN3A receptor

  • 期刊论文

  • 发表日期：

    2025年3月11日

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  • Ze-chen Wang,
  • Yue Zeng,
  • Jin-yuan Sun,
  • Xue-qin Chen,
  • Hao-chen Wu,
  • Yang-yang Li,
  • Yu-guang Mu,
  • Liang-zhen Zheng,
  • Zhao-bing Gao,
  • Wei-feng Li

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 μM 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.