Title:Machine Learning in the Rational Design of Antimicrobial Peptides
Volume: 10
Issue: 3
Author(s): Paola Rondon-Villarreal, Daniel A. Sierra and Rodrigo Torres
Affiliation:
Keywords:
Antimicrobial peptides, classification, descriptors, machine learning, QSAR, rational design.
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.
