Machine Learning in the Rational Design of Antimicrobial Peptides

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.

Cite this article as:

Rondon-Villarreal Paola, A. Sierra Daniel and Torres Rodrigo, Machine Learning in the Rational Design of Antimicrobial Peptides, Current Computer-Aided Drug Design 2014; 10 (3) . https://dx.doi.org/10.2174/1573409910666140624124807