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  3. A deep learning model for structure-based bioactivity optimization and its application in the bioactivity optimization of a SARS-CoV-2 main protease inhibitor

A deep learning model for structure-based bioactivity optimization and its application in the bioactivity optimization of a SARS-CoV-2 main protease inhibitor

  • Eur J Med Chem. 2025 Jul 5:291:117602. doi: 10.1016/j.ejmech.2025.117602.
Zhenyu Yang 1,  Kai Wang 2,  Guo Zhang 2,  Yuanyuan Jiang 2,  Rui Zeng 2,  Jingxin Qiao 2,  Yueyue Li 2,  Xinyue Deng 2,  Ziyi Xia 2,  Rui Yao 2,  Xiaoxi Zeng 1,  Liyun Zhang 3,  Yi Zhao 4,  Jian Lei 5,  Runsheng Chen 6
Affiliations

Affiliations

  • 1 West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China.
  • 2 Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China.
  • 3 Lead Generation Unit, HitGen Inc., Tianfu International Bio-Town, Shuangliu District, Chengdu, Sichuan, 610200, China.
  • 4 Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China. Electronic address: biozy@ict.ac.cn.
  • 5 Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China; National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China. Electronic address: leijian@scu.edu.cn.
  • 6 West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China; Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China. Electronic address: rschen@ibp.ac.cn.
PMID: 40239482 DOI: 10.1016/j.ejmech.2025.117602
Abstract

Bioactivity optimization is a crucial and technical task in the early stages of drug discovery, traditionally carried out through iterative substituent optimization, a process that is often both time-consuming and expensive. To address this challenge, we present Pocket-StrMod, a deep-learning model tailored for structure-based bioactivity optimization. Pocket-StrMod employs an autoregressive flow-based architecture, optimizing molecules within a specific protein binding pocket while explicitly incorporating chemical expertise. It synchronously optimizes all substituents by generating atoms and covalent bonds at designated sites within a molecular scaffold nestled inside a protein pocket. We applied this model to optimize the bioactivity of Hit1, an inhibitor of the SARS-CoV-2 main protease (Mpro) with initially poor bioactivity (IC50 : 34.56 μM). Following two rounds of optimization, six compounds were selected for synthesis and bioactivity testing. This led to the discovery of C5, a potent compound with an IC50 value of 33.6 nM, marking a remarkable 1028-fold improvement over Hit1. Furthermore, C5 demonstrated promising in vitro Antiviral activity against SARS-CoV-2. Collectively, these findings underscore the great potential of deep learning in facilitating rapid and cost-effective bioactivity optimization in the early phases of drug development.

Keywords

Deep-learning model; Molecular scaffold; Pocket-StrMod; SARS-CoV-2 main protease; Structure-based bioactivity optimization.

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