Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies

Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computation...

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Detalles Bibliográficos
Autores principales: Akbar, Rahmad, Bashour, Habib, Rawat, Puneet, Robert, Philippe A., Smorodina, Eva, Cotet, Tudor-Stefan, Flem-Karlsen, Karine, Frank, Robert, Mehta, Brij Bhushan, Vu, Mai Ha, Zengin, Talip, Gutierrez-Marcos, Jose, Lund-Johansen, Fridtjof, Andersen, Jan Terje, Greiff, Victor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2022
Materias:
Review
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8928824/
https://www.ncbi.nlm.nih.gov/pubmed/35293269
http://dx.doi.org/10.1080/19420862.2021.2008790

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8928824/
https://www.ncbi.nlm.nih.gov/pubmed/35293269
http://dx.doi.org/10.1080/19420862.2021.2008790

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