The application of artificial intelligence to accelerate G protein-coupled receptor drug discovery.

Journal: British journal of pharmacology
Published Date: May 26, 2023

Abstract

The application of artificial intelligence (AI) approaches to drug discovery for G protein-coupled receptors (GPCRs) is a rapidly expanding area. Artificial intelligence can be used at multiple stages during the drug discovery process, from aiding our understanding of the fundamental actions of GPCRs to the discovery of new ligand-GPCR interactions or the prediction of clinical responses. Here, we provide an overview of the concepts behind artificial intelligence, including the subfields of machine learning and deep learning. We summarise the published applications of artificial intelligence to different stages of the GPCR drug discovery process. Finally, we reflect on the benefits and limitations of artificial intelligence and share our vision for the exciting potential for further development of applications to aid GPCR drug discovery. In addition to making the drug discovery process "faster, smarter and cheaper," we anticipate that the application of artificial intelligence will create exciting new opportunities for GPCR drug discovery. LINKED ARTICLES: This article is part of a themed issue Therapeutic Targeting of G Protein-Coupled Receptors: hot topics from the Australasian Society of Clinical and Experimental Pharmacologists and Toxicologists 2021 Virtual Annual Scientific Meeting. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v181.14/issuetoc.

Authors

  • Anh T N Nguyen
    Drug Discovery Biology Theme, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia.
  • Diep T N Nguyen
    Department of Information Technology, Faculty of Engineering and Technology, Vietnam National University, Cau Giay, Hanoi, Vietnam.
  • Huan Yee Koh
    Drug Discovery Biology Theme, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia.
  • Jason Toskov
    Monash DeepNeuron, Monash University, Clayton, Victoria, Australia.
  • William MacLean
    Monash DeepNeuron, Monash University, Clayton, Victoria, Australia.
  • Andrew Xu
    Monash DeepNeuron, Monash University, Clayton, Victoria, Australia.
  • Daokun Zhang
    Drug Discovery Biology Theme, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia.
  • Geoffrey I Webb
    College of Information Engineering, Northwest A&F University, Yangling 712100, China, Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia, National Engineering Laboratory for Industrial Enzymes and Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China, Centre for Research in Intelligent Systems, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia and ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC 3800, Australia.
  • Lauren T May
    Drug Discovery Biology Theme, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia.
  • Michelle L Halls
    Drug Discovery Biology Theme, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia.

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