Machine Learning in the Rational Design of Antimicrobial Peptides

ISSN: 1875-6697 (Online)
ISSN: 1573-4099 (Print)


Volume 10, 4 Issues, 2014


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Editor-in-Chief:
Subhash C. Basak
Departments of Chemistry, Biochemistry & Molecular Biology University of Minnesota Duluth
Duluth, MN 55811
USA


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Machine Learning in the Rational Design of Antimicrobial Peptides

Author(s): Paola Rondón-Villarreal, Daniel A. Sierra and Rodrigo Torres

Affiliation: School of Electrical, Electronics and Telecommunications Engineering, Universidad Industrial de Santander, Bucaramanga, Colombia

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 antibiotic-resistant 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


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Article Details

Volume: 10
First Page: 1
Last Page: 1
Page Count: 1
DOI: 10.2174/1573409910666140624124807
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