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

MAbs. 2022 Jan-Dec;14(1):2008790. doi: 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.

Keywords: Machine learning; antibody; antigen; artificial intelligence; developability; drug design.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Algorithms
  • Antibodies, Monoclonal / therapeutic use
  • Antineoplastic Agents, Immunological*
  • Artificial Intelligence*
  • Machine Learning

Substances

  • Antibodies, Monoclonal
  • Antineoplastic Agents, Immunological

Grant support

We acknowledge generous support by The Leona M. and Harry B. Helmsley Charitable Trust (#2019PG-T1D011, to VG), UiO World-Leading Research Community (to VG), UiO:LifeScience Convergence Environment Immunolingo (to VG), EU Horizon 2020 iReceptorplus (#825821) (to VG), a Research Council of Norway FRIPRO project (#300740, to VG), a Research Council of Norway IKTPLUSS project (#311341, to VG), a Norwegian Cancer Society Grant (#215817, to VG), Research Council of Norway (#287927, to JTA and KFK) and a grant from the South-Eastern Norway Regional Health Authority (#2021069, to JTA and KFK).