title: Machine learning in the rational design of antimicrobial peptides.
abstract: One of the most important public health issues is the microbial and bacterial resistance to conventional antibiotics by pathogen microorganisms. In recent years, many researches have been focused on the development of new antibiotics. Among these, antimicrobial peptides (AMPs) have raised as a promising alternative to combat antibioticresistant microorganisms. For this reason, many theoretical efforts have been done in the development of new computational tools for the rational design of both better and effective AMPs. In this review, we present an overview of the rational design of AMPs using machine learning techniques and new research fields.
authors.affiliation: School of Electrical, Electronics and Telecommunications Engineering, Universidad Industrial de Santander, Carrera 27 calle 9, Ciudad Universitaria, Laboratorios Pesados 213B, Bucaramanga, Colombia. nydia.rondon@correo.uis.edu.co.
authors.forename: Paola
authors.initials: P
authors.lastname: Rondón-Villarreal
authors.forename: Daniel A
authors.initials: DA
authors.lastname: Sierra
authors.forename: Rodrigo
authors.initials: R
authors.lastname: Torres
mesh_terms: D000890:Anti-Infective Agents / Q000494:pharmacology*; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods; D015195:Drug Design; D004352:Drug Resistance, Microbial; D006801:Humans; D010455:Peptides / Q000494:pharmacology
chemical_list: D000890:Anti-Infective Agents; D010455:Peptides