Generative deep learning enables the discovery of a potent and selective RIPK1 inhibitor.

Journal: Nature communications
PMID: 36371441

Abstract

The retrieval of hit/lead compounds with novel scaffolds during early drug development is an important but challenging task. Various generative models have been proposed to create drug-like molecules. However, the capacity of these generative models to design wet-lab-validated and target-specific molecules with novel scaffolds has hardly been verified. We herein propose a generative deep learning (GDL) model, a distribution-learning conditional recurrent neural network (cRNN), to generate tailor-made virtual compound libraries for given biological targets. The GDL model is then applied to RIPK1. Virtual screening against the generated tailor-made compound library and subsequent bioactivity evaluation lead to the discovery of a potent and selective RIPK1 inhibitor with a previously unreported scaffold, RI-962. This compound displays potent in vitro activity in protecting cells from necroptosis, and good in vivo efficacy in two inflammatory models. Collectively, the findings prove the capacity of our GDL model in generating hit/lead compounds with unreported scaffolds, highlighting a great potential of deep learning in drug discovery.

Authors

  • Yueshan Li
    State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, 610041, Chengdu, Sichuan, China.
  • Liting Zhang
    School of Microelectronics, Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.
  • Yifei Wang
    Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Jun Zou
    State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, China.
  • Ruicheng Yang
    State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, 610041, Chengdu, Sichuan, China.
  • Xinling Luo
    Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, 610041, Chengdu, Sichuan, China.
  • Chengyong Wu
    State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, 610041, Chengdu, Sichuan, China.
  • Wei Yang
    Key Laboratory of Structure-Based Drug Design and Discovery (Shenyang Pharmaceutical University), Ministry of Education, School of Traditional Chinese Materia Medica, Shenyang Pharmaceutical University, Wenhua Road 103, Shenyang 110016, PR China. Electronic address: 421063202@qq.com.
  • Chenyu Tian
    State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, 610041, Chengdu, Sichuan, China.
  • Haixing Xu
    State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, 610041, Chengdu, Sichuan, China.
  • Falu Wang
    State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, 610041, Chengdu, Sichuan, China.
  • Xin Yang
    Department of Oral Maxillofacial-Head Neck Oncology, Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai, China.
  • Linli Li
    Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, 610041, Chengdu, Sichuan, China.
  • Shengyong Yang
    State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, China.

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