Accelerating the Discovery and Design of Antimicrobial Peptides with Artificial Intelligence.
Algorithmic bias
Antibiotics
Antimicrobial peptides
Antimicrobial resistance
Generative modeling
Machine learning
Predictive modeling
Representation learning
Journal
Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969
Informations de publication
Date de publication:
2024
2024
Historique:
medline:
8
9
2023
pubmed:
7
9
2023
entrez:
7
9
2023
Statut:
ppublish
Résumé
Peptides modulate many processes of human physiology targeting ion channels, protein receptors, or enzymes. They represent valuable starting points for the development of new biologics against communicable and non-communicable disorders. However, turning native peptide ligands into druggable materials requires high selectivity and efficacy, predictable metabolism, and good safety profiles. Machine learning models have gradually emerged as cost-effective and time-saving solutions to predict and generate new proteins with optimal properties. In this chapter, we will discuss the evolution and applications of predictive modeling and generative modeling to discover and design safe and effective antimicrobial peptides. We will also present their current limitations and suggest future research directions, applicable to peptide drug design campaigns.
Identifiants
pubmed: 37676607
doi: 10.1007/978-1-0716-3441-7_18
doi:
Substances chimiques
Antimicrobial Peptides
0
Biological Products
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
329-352Subventions
Organisme : NIGMS NIH HHS
ID : R35 GM138201
Pays : United States
Informations de copyright
© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.
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