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

   
@article{APS11424,
	author = {Li Han and Yue Zeng and Zhi-yan Qu and Sui Fang and Hai-ying Wang and Ya-shuo Dong and Xiang-ming Zeng and Tong-yan Zhang and Ze-bin Yu and Ling Kang and Zhao-bing Gao and Quan Guo},
	title = {Identification of small-molecule inhibitors for GluN1/GluN3A NMDA receptors via a multiscale CNN-based prediction model},
	journal = {Acta Pharmacologica Sinica},
	volume = {47},
	number = {1},
	year = {2025},
	keywords = {},
	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 },
	issn = {1745-7254},	url = {http://www.chinaphar.com/article/view/11424}
}