PROPERMAB: an integrative framework for in silico prediction of antibody developability using machine learning

PROPERMAB: an integrative framework for in silico prediction of antibody developability using machine learning

Selection of lead therapeutic molecules is often driven predominantly by pharmacological efficacy and safety. Candidate developability, such as biophysical properties that affect the formulation of the molecule into a product, is usually evaluated only toward the end of the drug development pipeline...

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Bibliographic Details
Main Authors: Bian Li, Shukun Luo, Wenhua Wang, Jiahui Xu, Dingjiang Liu, Mohammed Shameem, John Mattila, Matthew C. Franklin, Peter G. Hawkins, Gurinder S. Atwal
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:mAbs
Subjects:
Antibody developability
feature engineering
in silico prediction
machine learning
Online Access:https://www.tandfonline.com/doi/10.1080/19420862.2025.2474521
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https://www.tandfonline.com/doi/10.1080/19420862.2025.2474521

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