AI-driven GPCR analysis, engineering, and targeting.

Journal: Current opinion in pharmacology
Published Date: Jan 13, 2024

Abstract

This article investigates the role of recent advances in Artificial Intelligence (AI) to revolutionise the study of G protein-coupled receptors (GPCRs). AI has been applied to many areas of GPCR research, including the application of machine learning (ML) in GPCR classification, prediction of GPCR activation levels, modelling GPCR 3D structures and interactions, understanding G-protein selectivity, aiding elucidation of GPCRs structures, and drug design. Despite progress, challenges in predicting GPCR structures and addressing the complex nature of GPCRs remain, providing avenues for future research and development.

Authors

  • João P L Velloso
    Structural Biology and Bioinformatics, Department of Biochemistry and Pharmacology, University of Melbourne, Melbourne, Victoria, Australia; Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia; Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Queensland, Australia.
  • Aaron S Kovacs
    Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Queensland, Australia.
  • Douglas E V Pires
    Department of Biochemistry and Molecular Biology, University of Melbourne, Melbourne, Australia.
  • David B Ascher
    Department of Biochemistry and Molecular Biology, University of Melbourne, Melbourne, Australia.

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