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Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies
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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, José F., Lund-Johansen, Fridtjof, Andersen, Jan Terje and Greiff, Victor (2022) Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies. mAbs, 14 (1). 2008790. doi:10.1080/19420862.2021.2008790 ISSN 1942-0870.
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Official URL: https://doi.org/10.1080/19420862.2021.2008790
Abstract
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.
| Item Type: | Journal Article | ||||||||||||||||||||||||
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| Subjects: | Q Science > Q Science (General) Q Science > QR Microbiology R Medicine > RS Pharmacy and materia medica T Technology > TA Engineering (General). Civil engineering (General) |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) | ||||||||||||||||||||||||
| SWORD Depositor: | Library Publications Router | ||||||||||||||||||||||||
| Library of Congress Subject Headings (LCSH): | Monoclonal antibodies, Monoclonal antibodies -- Computer simulation, Machine learning, Artificial intelligence, Antigens, Drugs -- Design | ||||||||||||||||||||||||
| Journal or Publication Title: | mAbs | ||||||||||||||||||||||||
| Publisher: | Informa UK Limited | ||||||||||||||||||||||||
| ISSN: | 1942-0870 | ||||||||||||||||||||||||
| Official Date: | 2022 | ||||||||||||||||||||||||
| Dates: |
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| Volume: | 14 | ||||||||||||||||||||||||
| Number: | 1 | ||||||||||||||||||||||||
| Article Number: | 2008790 | ||||||||||||||||||||||||
| DOI: | 10.1080/19420862.2021.2008790 | ||||||||||||||||||||||||
| Status: | Peer Reviewed | ||||||||||||||||||||||||
| Publication Status: | Published | ||||||||||||||||||||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||||||||||||||
| Date of first compliant deposit: | 5 April 2022 | ||||||||||||||||||||||||
| Date of first compliant Open Access: | 5 April 2022 | ||||||||||||||||||||||||
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