DLAB: deep learning methods for structure-based virtual screening of antibodies.

Journal: Bioinformatics (Oxford, England)
Published Date: Jan 3, 2022

Abstract

MOTIVATION: Antibodies are one of the most important classes of pharmaceuticals, with over 80 approved molecules currently in use against a wide variety of diseases. The drug discovery process for antibody therapeutic candidates however is time- and cost-intensive and heavily reliant on in vivo and in vitro high throughput screens. Here, we introduce a framework for structure-based deep learning for antibodies (DLAB) which can virtually screen putative binding antibodies against antigen targets of interest. DLAB is built to be able to predict antibody-antigen binding for antigens with no known antibody binders.

Authors

  • Constantin Schneider
    Department of Statistics, University of Oxford, Oxford, OX1 3LB, UK.
  • Andrew Buchanan
    Antibody Discovery & Protein Engineering, R&D, AstraZeneca, Cambridge, CB2 0AA, UK.
  • Bruck Taddese
    Discovery Sciences, R&D, AstraZeneca, Cambridge, CB2 0AA, UK.
  • Charlotte M Deane
    Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, United Kingdom.

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