Computational and artificial intelligence-based methods for antibody development.
Trends Pharmacol Sci
; 44(3): 175-189, 2023 03.
Article
em En
| MEDLINE
| ID: mdl-36669976
ABSTRACT
Due to their high target specificity and binding affinity, therapeutic antibodies are currently the largest class of biotherapeutics. The traditional largely empirical antibody development process is, while mature and robust, cumbersome and has significant limitations. Substantial recent advances in computational and artificial intelligence (AI) technologies are now starting to overcome many of these limitations and are increasingly integrated into development pipelines. Here, we provide an overview of AI methods relevant for antibody development, including databases, computational predictors of antibody properties and structure, and computational antibody design methods with an emphasis on machine learning (ML) models, and the design of complementarity-determining region (CDR) loops, antibody structural components critical for binding.
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Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Inteligência Artificial
/
Anticorpos
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article