Applying artificial intelligence to accelerate and de-risk antibody discovery

As in all sectors of science and industry, artificial intelligence (AI) is meant to have a high impact in the discovery of antibodies in the coming years. Antibody discovery was traditionally conducted through a succession of experimental steps: animal immunization, screening of relevant clones, in...

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Bibliographic Details
Published in:Frontiers in drug discovery 2024-03, Vol.4
Main Authors: Musnier, Astrid, Dumet, Christophe, Mitra, Saheli, Verdier, Adrien, Keskes, Raouf, Chassine, Augustin, Jullian, Yann, Cortes, Mélanie, Corde, Yannick, Omahdi, Zakaria, Puard, Vincent, Bourquard, Thomas, Poupon, Anne
Format: Article
Language:English
Subjects:
affinity
antibody
Artificial Intelligence
Chemical Sciences
Cheminformatics
Computer Science
discovery
Immunology
Immunotherapy
Life Sciences
manufacturability
Quantitative Methods
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Summary:As in all sectors of science and industry, artificial intelligence (AI) is meant to have a high impact in the discovery of antibodies in the coming years. Antibody discovery was traditionally conducted through a succession of experimental steps: animal immunization, screening of relevant clones, in vitro testing, affinity maturation, in vivo testing in animal models, then different steps of humanization and maturation generating the candidate that will be tested in clinical trials. This scheme suffers from different flaws, rendering the whole process very risky, with an attrition rate over 95%. The rise of in silico methods, among which AI, has been gradually proven to reliably guide different experimental steps with more robust processes. They are now capable of covering the whole discovery process. Amongst the players in this new field, the company MAbSilico proposes an in silico pipeline allowing to design antibody sequences in a few days, already humanized and optimized for affinity and developability, considerably de-risking and accelerating the discovery process.
ISSN:2674-0338
2674-0338
DOI:10.3389/fddsv.2024.1339697