F-CPI: A Multimodal Deep Learning Approach for Predicting Compound Bioactivity Changes Induced by Fluorine Substitution.

Journal: Journal of medicinal chemistry
PMID: 39707149

Abstract

Fluorine (F) substitution is a common method of drug discovery and development. However, there are no accurate approaches available for predicting the bioactivity changes after F-substitution, as the effect of substitution on the interactions between compounds and proteins (CPI) remains a mystery. In this study, we constructed a data set with 111,168 pairs of fluorine-substituted and nonfluorine-substituted compounds. We developed a multimodal deep learning model (F-CPI). In comparison with traditional machine learning and popular CPI task models, the accuracy, precision, and recall of F-CPI (∼90, ∼79, and ∼45%) were higher than those of GraphDTA (∼86, ∼58, and ∼40%). The application of the F-CPI for the structural optimization of hit compounds against SARS-CoV-2 3CL by F-substitution achieved a more than 100-fold increase in bioactivity (IC: 0.23 μM vs 28.19 μM). Therefore, the multimodal deep learning model F-CPI would be a veritable and effective tool in the context of drug discovery and design.

Authors

  • Qian Zhang
    The Neonatal Intensive Care Unit, Peking Union Medical College Hospital, Peking, China.
  • Wenhai Yin
    School of Computer Science and Technology, Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, East China Normal University, Shanghai 200241, China.
  • Xinyao Chen
    Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang 110016, China.
  • Aimin Zhou
    School of Design Art, Lanzhou University of Technology, Lanzhou 730050, China.
  • Guixu Zhang
  • Zhi Zhao
    Department of Orthopedics, Laboratory of Tissue and Transplant in Anhui Province, the First Affiliated Hospital of Bengbu Medical College, Bengbu Anhui, 233003, P.R.China.
  • Zhiqiang Li
    The Affiliated Hospital of Qingdao University, The Biomedical Sciences Institute of Qingdao University (Qingdao Branch of SJTU Bio-X Institutes), Qingdao University, Qingdao, 266003, China.
  • Yan Zhang
    Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang, 110032, China.
  • Samuel Jacob Bunu
    State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
  • Jingshan Shen
    CAS Key Laboratory for Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China.
  • Weiliang Zhu
    CAS Key Laboratory of Receptor Research, Stake Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
  • Xiangrui Jiang
    CAS Key Laboratory for Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China.
  • Zhijian Xu
    Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.

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