Optimization of therapeutic antibodies for reduced self-association and non-specific binding via interpretable machine learning-Reference-Cited by-同舟云学术

Optimization of therapeutic antibodies for reduced self-association and non-specific binding via interpretable machine learning

Published:2023-09-04 Issue:1 Volume:8 Page:45-56
ISSN:2157-846X
Container-title:Nature Biomedical Engineering
language:en
Short-container-title:Nat. Biomed. Eng

Author:

Makowski Emily K.^ORCID,Wang Tiexin,Zupancic Jennifer M.,Huang Jie,Wu Lina,Schardt John S.,De Groot Anne S.,Elkins Stephanie L.,Martin William D.,Tessier Peter M.^ORCID

Funder

Foundation for the National Institutes of Health

National Science Foundation

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Biomedical Engineering,Medicine (miscellaneous),Bioengineering,Biotechnology

Link

https://www.nature.com/articles/s41551-023-01074-6.pdf

Reference74 articles.

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3. Labrijn, A. F., Janmaat, M. L., Reichert, J. M. & Parren, P. Bispecific antibodies: a mechanistic review of the pipeline. Nat. Rev. Drug Discov. 18, 585–608 (2019).

4. Shim, H. Bispecific antibodies and antibody-drug conjugates for cancer therapy: technological considerations. Biomolecules 10, 360 (2020).

5. Drago, J. Z., Modi, S. & Chandarlapaty, S. Unlocking the potential of antibody–drug conjugates for cancer therapy. Nat. Rev. Clin. Oncol. 18, 327–344 (2021).

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