Accelerating the Discovery and Design of Antimicrobial Peptides with Artificial Intelligence

MARIANA D. C. AGUILERA-PUGA; NATALIA L. CANCELARICH; MARIELA M. MARANI; CESAR DE LA FUENTE-NUNEZ; FABIEN PLISSON

Computational Drug Discovery and Design

Springer Nature

Lugar: Nueva York; Año: 2023; p. 329 - 352

Peptides modulate many processes of human physiology targeting ion channels, protein receptors, orenzymes. They represent valuable starting points for the development of new biologics against communi-cable and non-communicable disorders. However, turning native peptide ligands into druggable materialsrequires high selectivity and efficacy, predictable metabolism, and good safety profiles. Machine learningmodels have gradually emerged as cost-effective and time-saving solutions to predict and generate newproteins with optimal properties. In this chapter, we will discuss the evolution and applications of predictivemodeling and generative modeling to discover and design safe and effective antimicrobial peptides. We willalso present their current limitations and suggest future research directions, applicable to peptide drugdesign campaigns.