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

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

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 incorpor...

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Bibliographic Details
Main Authors: Frédéric A. Dreyer, Constantin Schneider, Aleksandr Kovaltsuk, Daniel Cutting, Matthew J. Byrne, Daniel A. Nissley, Henry Kenlay, Claire Marks, David Errington, Richard J. Gildea, David Damerell, Pedro Tizei, Wilawan Bunjobpol, John F. Darby, Ieva Drulyte, Daniel L. Hurdiss, Sachin Surade, Newton Wahome, Douglas E.V. Pires, Charlotte M. Deane
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:mAbs
Subjects:
Antibody design
artificial intelligence
immunology
mab
structural biology
Online Access:https://www.tandfonline.com/doi/10.1080/19420862.2025.2511220
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https://www.tandfonline.com/doi/10.1080/19420862.2025.2511220

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