Deep Learning for Antimicrobial Peptides: Computational Models and Databases.

Xiangrun Zhou, Guixia Liu, Shuyuan Cao, Ji Lv
Author Information
  1. Xiangrun Zhou: College of Computer Science and Technology, Jilin University, Changchun, 130000, China. ORCID
  2. Guixia Liu: College of Computer Science and Technology, Jilin University, Changchun, 130000, China.
  3. Shuyuan Cao: College of Computer Science and Technology, Jilin University, Changchun, 130000, China.
  4. Ji Lv: School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, China. ORCID

Abstract

Antimicrobial peptides are a promising strategy to combat antimicrobial resistance. However, the experimental discovery of antimicrobial peptides is both time-consuming and laborious. In recent years, the development of computational technologies (especially deep learning) has provided new opportunities for antimicrobial peptide prediction. Various computational models have been proposed to predict antimicrobial peptide. In this review, we focus on deep learning models for antimicrobial peptide prediction. We first collected and summarized available data resources for antimicrobial peptides. Subsequently, we summarized existing deep learning models for antimicrobial peptides and discussed their limitations and challenges. This study aims to help computational biologists design better deep learning models for antimicrobial peptide prediction.

Keywords

MeSH Term

Deep Learning
Antimicrobial Peptides
Computational Biology
Databases, Protein
Humans
Computer Simulation
Models, Molecular

Chemicals

Antimicrobial Peptides

Word Cloud

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