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使用集成AI和基于物理的方法发现选择性GluN1/GluN3ANMDA受体抑制剂

Shi-Wei Li¹, Yue Zeng^2,3,4, Sa-Nan Wu¹

¹Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China.

Acta pharmacologica Sinica

|July 14, 2025

概括

研究人员开发了一种人工智能驱动的管道,以找到GluN1/GluN3A受体的抑制剂,这对于情绪调节至关重要. 这种方法成功地确定了强效和选择性化合物,推进了潜在的N-甲基-D-酸盐受体 (NMDAR) 治疗方法.

科学领域:

神经科学是一个神经科学.
药理学药理学是指药理学的学科.
计算化学的计算化学

背景情况:

N-甲基-D-酸盐受体 (NMDARs) 是对大脑功能至关重要的离子通道.
非传统的GluN1/GluN3A NMDAR亚型,作为激发性甘氨酸受体,与情绪调节有关,但缺乏特定调节器.
在NMDAR研究中的挑战包括有限的结构数据和固有的离子通道复杂性.

研究的目的:

开发和验证人工智能辅助的虚拟查管道,用于识别NMDAR抑制剂.
发现GluN1/GluN3A受体亚型的强效和选择性调节剂.

主要方法:

整合了两个基于序列的深度学习模型 (TEFDTA和ESMLigSite) 与分子对接.
选一个商业数据库,包括1800万个化合物,以寻找潜在的GluN1/GluN3A抑制剂.

主要成果:

在识别潜在抑制剂方面实现了50%的成功率.
发现一种具有强烈抑制活性的化合物 (IC50 = 1.26 ± 0.23 μM).
证明了显著的目标特异性,显示GluN1/GluN3A对GluN1/GluN2A的选择性超过23倍.

结论:

人工智能辅助的虚拟选对于针对NMDARs等复杂离子通道是有效的.
已识别的化合物代表了针对情绪障碍的治疗探索的有希望的候选人.
这条管道为药物发现提供了一种新的策略,用于对抗具有挑战性的生物目标.

关键词:

在N-甲基-D-酸盐受体.在GluN1/GluN3A中使用.具有约束力的网站识别.深度学习是一种深度学习.分子对接的分子对接.虚拟选虚拟选虚拟选

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