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Peer-Reviewed Publication
Drug Discov Today2024;29(7):104025.July 1, 2024Journal Article

Best practices for machine learning in antibody discovery and development.

Leonard Wossnig1, Norbert Furtmann2, Andrew Buchanan3, Sandeep Kumar4, Victor Greiff5
1LabGenius Ltd, The Biscuit Factory, 100 Drummond Road, London SE16 4DG, UK; Department of Computer Science, University College London, 66-72 Gower St, London WC1E 6EA, UK. Electronic address: leonard.wossnig@labgeni.us.
2R&D Large Molecules Research Platform, Sanofi Deutschland GmbH, Industriepark Höchst, Frankfurt Am Main, Germany.
3Biologics Engineering, R&D, AstraZeneca, Cambridge CB2 0AA, UK.
4Computational Protein Design and Modeling Group, Computational Science, Moderna Therapeutics, 200 Technology Square, Cambridge, MA 02139, USA.
5Department of Immunology and Oslo University Hospital, University of Oslo, Oslo, Norway.
PubMed DOI

Abstract

In the past 40 years, therapeutic antibody discovery and development have advanced considerably, with machine learning (ML) offering a promising way to speed up the process by reducing costs and the number of experiments required. Recent progress in ML-guided antibody design and development (D&D) has been hindered by the diversity of data sets and evaluation methods, which makes it difficult to co…

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