Han Li, Zeng Yue, Qu Zhi-Yan, Fang Sui, Wang Hai-Ying, Dong Ya-Shuo, Zeng Xiang-Ming, Zhang Tong-Yan, Yu Ze-Bin, Kang Ling, Gao Zhao-Bing, Guo Quan
Software and Big Data Technology Department, Dalian Neusoft University of Information, Dalian, 116023, China.
State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
Acta Pharmacol Sin. 2025 Aug 12. doi: 10.1038/s41401-025-01630-7.
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.
N-甲基-D-天冬氨酸受体（NMDARs）是兴奋性神经传递的关键介质，由七个亚基（GluN1、GluN2A-D和GluN3A-B）组成，这些亚基形成了多种受体亚型。虽然GluN1/GluN2亚型已得到广泛研究并催生了获批的治疗药物，但尽管有新证据表明GluN1/GluN3A亚型与神经精神疾病有关，它仍未得到充分探索。高效识别调节剂需要准确预测药物-靶点亲和力（DTA），尤其是对于像GluN1/GluN3A这样具有挑战性的靶点。在本研究中，我们应用了ImageDTA模型，这是一种多尺度二维卷积神经网络（CNN），对1800万个小分子进行虚拟筛选，以寻找GluN1/GluN3A抑制剂。这种人工智能（AI）驱动的方法对12种化合物进行了优先排序，其中三种在实验验证中表现出强效抑制活性（IC₅₀ < 30 µM）。最有效的命中化合物，IC为4.16 ± 0.65 µM，揭示了一种新的结构支架，从而突出了AI在加速未充分探索的受体亚型药物发现方面的潜力。这些发现为推进以GluN1/GluN3A为靶点的治疗方法建立了一个强大的框架。
