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AI-enhanced virtual screening approach to hit identification for GluN1/GluN3A NMDA receptor

   
@article{APS11425,
	author = {Yue-shan Ji and Yue Zeng and Shao-fei Hu and Shu-wang Li and Bei-chen Zhang and Chang Liu and Hao-chen Wu and An-yang Wang and Zhao-bing Gao and Yue Kong},
	title = {AI-enhanced virtual screening approach to hit identification for GluN1/GluN3A NMDA receptor},
	journal = {Acta Pharmacologica Sinica},
	volume = {47},
	number = {1},
	year = {2025},
	keywords = {},
	abstract = {N-methyl-D-aspartate receptors (NMDARs) are calcium-permeable ionotropic glutamate receptors broadly expressed throughout the central nervous system, where they play crucial roles in neuronal development and synaptic plasticity. Among the various subtypes, the GluN1/GluN3A receptor represents a unique glycine-gated NMDAR with notably low calcium permeability. Despite its distinctive properties, GluN1/GluN3A remains understudied, particularly with respect to pharmacological tools development. This scarcity poses challenges for deeper investigation into its physiological functions and therapeutic relevance. In this study, we employed a hybrid virtual screening (VS) pipeline that integrates ligand-based and structure-based approaches for the efficient and precise identification of small-molecule candidates targeting GluN1/GluN3A. A large compound library comprising 18 million molecules was screened using an AI-enhanced multi-stage method. The initial phase utilized shape similarity ranking via ROCS-BART, followed by refinement with a graph neural network (GNN)-based drug-target interaction model to enhance docking accuracy. Functional validation using calcium flux (FDSS/μCell) identified two compounds with IC50 values below 10 μM. Of these, one candidate exhibited potent inhibitory activity with an IC50 of 5.31 ± 1.65 μM, which was further confirmed through manual patch-clamp recordings. These findings highlight an AI-enhanced VS workflow that achieves both efficiency and precision, providing a promising framework for exploring elusive targets such as GluN1/GluN3A.},
	issn = {1745-7254},	url = {http://www.chinaphar.com/article/view/11425}
}