Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies.
MAbs
; 14(1): 2008790, 2022.
Article
en En
| MEDLINE
| ID: mdl-35293269
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Inteligencia Artificial
/
Antineoplásicos Inmunológicos
Idioma:
En
Revista:
MAbs
Asunto de la revista:
ALERGIA E IMUNOLOGIA
Año:
2022
Tipo del documento:
Article
País de afiliación:
Noruega