Machine Learning in the Rational Design of Antimicrobial Peptides
Paola Rondon-Villarreal, Daniel A. Sierra and Rodrigo TorresAffiliation:
School of Electrical, Electronics and Telecommunications Engineering, Universidad Industrial de Santander, Carrera 27 calle 9, Ciudad Universitaria, Laboratorios Pesados 213B, Bucaramanga, Colombia.
AbstractOne 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.
Antimicrobial peptides, classification, descriptors, machine learning, QSAR, rational design.
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