Biophysical cartography of the native and human-engineered antibody landscapes quantifies the plasticity of antibody developability-Reference-Cited by-同舟云学术

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Biophysical cartography of the native and human-engineered antibody landscapes quantifies the plasticity of antibody developability

Published:2024-07-31 Issue:1 Volume:7 Page:
ISSN:2399-3642
Container-title:Communications Biology
language:en
Short-container-title:Commun Biol

Author:

Bashour Habib^ORCID,Smorodina Eva^ORCID,Pariset Matteo,Zhong Jahn^ORCID,Akbar Rahmad^ORCID,Chernigovskaya Maria,Lê Quý Khang,Snapkow Igor,Rawat Puneet,Krawczyk Konrad^ORCID,Sandve Geir Kjetil^ORCID,Gutierrez-Marcos Jose^ORCID,Gutierrez Daniel Nakhaee-Zadeh,Andersen Jan Terje^ORCID,Greiff Victor^ORCID

Publisher

Springer Science and Business Media LLC

Link

https://www.nature.com/articles/s42003-024-06561-3.pdf

Reference194 articles.

1. Singh, S. et al. Monoclonal antibodies: a review. Curr. Clin. Pharmacol. 13, 85–99 (2018).

2. Khetan, R. et al. Current advances in biopharmaceutical informatics: guidelines, impact and challenges in the computational developability assessment of antibody therapeutics. MAbs 14, 2020082 (2022).

3. Akbar, R. et al. Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies. MAbs 14, 2008790 (2022).

4. Laustsen, A. H., Greiff, V., Karatt-Vellatt, A., Muyldermans, S. & Jenkins, T. P. Animal immunization, in vitro display technologies, and machine learning for antibody discovery. Trends Biotechnol. 39, 1263–1273 (2021).

5. Wilman, W. et al. Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery. Brief. Bioinform. 23, bbac267 (2022).

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