Computational design of therapeutic antibodies with improved developability: efficient traversal of binder landscapes and rescue of escape mutations.

Frédéric A Dreyer¹, Constantin Schneider¹, Aleksandr Kovaltsuk¹, Daniel Cutting¹, Matthew J Byrne¹, Daniel A Nissley¹, Henry Kenlay¹, Claire Marks¹, David Errington¹, Richard J Gildea¹, David Damerell¹, Pedro Tizei¹, Wilawan Bunjobpol¹, John F Darby¹, Ieva Drulyte², Daniel L Hurdiss³, Sachin Surade¹, Newton Wahome¹, Douglas E V Pires¹, Charlotte M Deane^1,4

¹Exscientia, Oxford Science Park, Oxford, UK.

²Materials and Structural Analysis, Thermo Fisher Scientific, Eindhoven, Netherlands.

³Virology Section, Infectious Diseases and Immunology Division, Department of Biomolecular Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands.

⁴Department of Statistics, University of Oxford, Oxford, UK.

PubMed PMC DOI PDF

Abstract

Developing therapeutic antibodies is a challenging endeavor, often requiring large-scale screening to produce initial binders, that still often require optimization for developability. We present a computational pipeline for the discovery and design of therapeutic antibody candidates, which incorporates physics- and AI-based methods for the generation, assessment, and validation of candidate antib…

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