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Discovery of novel GluN1/GluN3A NMDA receptor inhibitors using a deep learning-based method

   
@article{APS11397,
	author = {Shi-hang Wang and Yue Zeng and Hao Yang and Si-yuan Tian and Yong-qi Zhou and Lin Wang and Xue-qin Chen and Hai-ying Wang and Zhao-bing Gao and Fang Bai},
	title = {Discovery of novel GluN1/GluN3A NMDA receptor inhibitors using a deep learning-based method},
	journal = {Acta Pharmacologica Sinica},
	volume = {46},
	number = {11},
	year = {2025},
	keywords = {},
	abstract = {Ligand-based drug discovery methods typically utilize pharmacophore similarities among molecules to screen for potential active compounds. Among these, scaffold hopping is a widely used ligand-based lead identification strategy that facilitates clinical candidate discovery by seeking inhibitors with similar biological activity yet distinct scaffolds. In this study, we employed GeminiMol, a deep learning-based molecular representation framework that incorporates bioactive conformational space information. This approach enables ligand-based virtual screening by referencing known active compounds to identify potential hits with similar structural and bioactive conformational features. Using GeminiMol-based ligand screening method, we discovered a potent GluN1/GluN3A inhibitor, GM-10, from an 18-million-compound library. Notably, GM-10 features a completely different scaffold compared to known inhibitors. Subsequent validation using whole-cell patch-clamp recording confirmed its activity, with an IC50 of 0.98 ± 0.13 μM for GluN1/GluN3A. Further optimization is required to enhance its selectivity, as it exhibited IC50 values of 3.89 ± 0.79 μM for GluN1/GluN2A and 1.03 ± 0.21 μM for GluN1/GluN3B. This work highlights the potential of AI-driven molecular representation technologies to facilitate scaffold hopping and enhance similarity-based virtual screening for drug discovery.},
	issn = {1745-7254},	url = {http://www.chinaphar.com/article/view/11397}
}