Computational and artificial intelligence-based methods for antibody development.

Journal: Trends in pharmacological sciences
PMID: 36669976

Abstract

Due to their high target specificity and binding affinity, therapeutic antibodies are currently the largest class of biotherapeutics. The traditional largely empirical antibody development process is, while mature and robust, cumbersome and has significant limitations. Substantial recent advances in computational and artificial intelligence (AI) technologies are now starting to overcome many of these limitations and are increasingly integrated into development pipelines. Here, we provide an overview of AI methods relevant for antibody development, including databases, computational predictors of antibody properties and structure, and computational antibody design methods with an emphasis on machine learning (ML) models, and the design of complementarity-determining region (CDR) loops, antibody structural components critical for binding.

Authors

  • Jisun Kim
    Division of Breast Surgery, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine.
  • Matthew McFee
    Department of Molecular Genetics, University of Toronto, Toronto M5S 1A8, Canada.
  • Qiao Fang
    Department of Restorative Dentistry, College of Dentistry, University of Illinois Chicago, Chicago, Illinois, USA.
  • Osama Abdin
    Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
  • Philip M Kim
    Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 1AS, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5S 3G4, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1AS, Canada. Electronic address: pi@kimlab.org.

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