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

• Main Authors: Rahmad Akbar (Author), Habib Bashour (Author), Puneet Rawat (Author), Philippe A. Robert (Author), Eva Smorodina (Author), Tudor-Stefan Cotet (Author), Karine Flem-Karlsen (Author), Robert Frank (Author), Brij Bhushan Mehta (Author), Mai Ha Vu (Author), Talip Zengin (Author), Jose Gutierrez-Marcos (Author), Fridtjof Lund-Johansen (Author), Jan Terje Andersen (Author), Victor Greiff (Author)
• Format: Book
• Published: Taylor & Francis Group, 2022-12-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

Summary

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 Description

10.1080/19420862.2021.2008790
1942-0870
1942-0862