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

Acta Pharmacol Sin. 2025 May 12. doi: 10.1038/s41401-025-01571-1.

Shi-Hang Wang ^# ¹ ², Yue Zeng ^# ³ ⁴ ⁵, Hao Yang ^# ¹ ², Si-Yuan Tian ^# ¹ ², Yong-Qi Zhou ^# ¹ ², Lin Wang ^# ¹ ², Xue-Qin Chen ³, Hai-Ying Wang ² ³, Zhao-Bing Gao ⁶ ⁷ ⁸, Fang Bai ⁹ ¹⁰ ¹¹ ¹²

Affiliations

1 Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, Shanghai, 201210, China.
2 School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China.
3 State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
4 University of Chinese Academy of Sciences, Beijing, 100049, China.
5 Department of Pharmacology, School of Pharmacy, Fudan University, Shanghai, 200032, China.
6 State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China. zbgao@simm.ac.cn.
7 University of Chinese Academy of Sciences, Beijing, 100049, China. zbgao@simm.ac.cn.
8 Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan, 528400, China. zbgao@simm.ac.cn.
9 Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, Shanghai, 201210, China. baifang@shanghaitech.edu.cn.
10 School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China. baifang@shanghaitech.edu.cn.
11 School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China. baifang@shanghaitech.edu.cn.
12 Shanghai Clinical Research and Trial Center, Shanghai, 201210, China. baifang@shanghaitech.edu.cn.
# Contributed equally.

PMID: 40355656 DOI: 10.1038/s41401-025-01571-1

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.

Keywords

N-methyl-D-aspartate receptors; GeminiMol; GluN1/GluN3A; deep learning; ligand-based virtual screening; scaffold hopping.

Products

Cat. No.

Product Name

Description

Target

Research Area
HY-172997

GluN1/3A-IN-1

GluN1/GluN3A Inhibitor

iGluR

Neurological Disease

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