Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies
| Autor: | Rahmad Akbar, Habib Bashour, Puneet Rawat, Philippe A. Robert, Eva Smorodina, Tudor-Stefan Cotet, Karine Flem-Karlsen, Robert Frank, Brij Bhushan Mehta, Mai Ha Vu, Talip Zengin, Jose Gutierrez-Marcos, Fridtjof Lund-Johansen, Jan Terje Andersen, Victor Greiff |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | mAbs, Vol 14, Iss 1 (2022) |
| Druh dokumentu: | article |
| ISSN: | 19420862 1942-0870 1942-0862 |
| DOI: | 10.1080/19420862.2021.2008790 |
| Popis: | 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, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates. |
| Databáze: | Directory of Open Access Journals |
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