Capítulo de Libro
Accelerating the Discovery and Design of Antimicrobial Peptides with Artificial Intelligence
Título del libro: Computational Drug Discovery and Design
Aguilera Puga, Mariana d. C.; Cancelarich, Natalia Lorena
; Marani, Mariela Mirta
; de la Fuente Nunez, Cesar; Plisson, Fabien Gérard Christian
Fecha de publicación:
2024
Editorial:
Springer Nature Switzerland AG
ISSN:
1064-3745
e-ISSN:
1940-6029
ISBN:
978-1-0716-3440-0
Idioma:
Inglés
Clasificación temática:
Resumen
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
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Capítulos de libros de INSTITUTO PATAGONICO PARA EL ESTUDIO DE LOS ECOSISTEMAS CONTINENTALES
Capítulos de libros de INSTITUTO PATAGONICO PARA EL ESTUDIO DE LOS ECOSISTEMAS CONTINENTALES
Citación
Aguilera Puga, Mariana d. C.; Cancelarich, Natalia Lorena; Marani, Mariela Mirta; de la Fuente Nunez, Cesar; Plisson, Fabien Gérard Christian; Accelerating the Discovery and Design of Antimicrobial Peptides with Artificial Intelligence; Springer Nature Switzerland AG; 2714; 2024; 329-351
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