2. Academic Validation
  3. Deep learning-assisted discovery of a potent and cell-active inhibitor of RNA N6-methyladenosine recognition protein YTHDC2

Deep learning-assisted discovery of a potent and cell-active inhibitor of RNA N6-methyladenosine recognition protein YTHDC2

  • Nat Commun. 2026 Jan 6;17(1):46. doi: 10.1038/s41467-025-65542-0.
Zhenyu Yang # 1,  Weining Sun # 2,  Qiao Huang # 2,  Yueyue Li # 2,  Meng Yuan 2,  Yu Yang 3,  Heng Zhao 4,  Zheyi Liu 4,  Xiaoxi Zeng 1,  Fangjun Wang 4,  Yuanyuan Jiang 5,  Yi Zhao 6 7,  Runsheng Chen 8 9
Affiliations

Affiliations

  • 1 West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • 2 Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • 3 Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.
  • 4 State Key Laboratory of Chemical Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China.
  • 5 Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China. yyjiang@scu.edu.cn.
  • 6 West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China. biozy@ict.ac.cn.
  • 7 Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China. biozy@ict.ac.cn.
  • 8 West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China. crs@ibp.ac.cn.
  • 9 Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China. crs@ibp.ac.cn.
  • # Contributed equally.
PMID: 41495018 DOI: 10.1038/s41467-025-65542-0
Abstract

YTHDC2, a unique YTH-domain-containing protein that recognizes N6-methyladenosine (m6A) on RNA, plays critical roles in diverse pathological processes and represents a promising therapeutic target. Despite its potential, no potent small-molecule inhibitors have been reported to date. To bridge this gap, we develop EPMolGen, a deep learning-based molecular generative model that explicitly incorporates the electrostatic features of Receptor Proteins. The model achieves state-of-the-art performance in dry-lab validations. Using EPMolGen, we identify H3, a YTHDC2 Inhibitor with an IC50 of 16.84 μM. Subsequent structural optimization of H3 yields DC2-C1, a highly potent compound with an IC50 of 0.168 μM against YTHDC2 and selectivity over Other YTH-domain proteins. In cellular assays, DC2-C1 effectively targets YTHDC2. Notably, DC2-C1 treatment substantially reduces the expression levels of multiple target mRNAs of YTHDC2, leading to phenotypic suppression of related cells. Overall, this study highlights the great potential of deep learning in drug discovery and provides a promising lead compound for drug development targeting YTHDC2.

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