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Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning
Yan, Jielu1; Cai, Jianxiu2,3; Zhang, Bob1; Wang, Yapeng2; Wong, Derek F.4; Siu, Shirley W.I.3,5
2022-10-01
Source PublicationAntibiotics
ISSN2079-6382
Volume11Issue:10
Abstract

Antimicrobial resistance has become a critical global health problem due to the abuse of conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides (AMPs) are a group of natural peptides that show promise as next-generation antibiotics due to their low toxicity to the host, broad spectrum of biological activity, including antibacterial, antifungal, antiviral, and anti-parasitic activities, and great therapeutic potential, such as anticancer, anti-inflammatory, etc. Most importantly, AMPs kill bacteria by damaging cell membranes using multiple mechanisms of action rather than targeting a single molecule or pathway, making it difficult for bacterial drug resistance to develop. However, experimental approaches used to discover and design new AMPs are very expensive and time-consuming. In recent years, there has been considerable interest in using in silico methods, including traditional machine learning (ML) and deep learning (DL) approaches, to drug discovery. While there are a few papers summarizing computational AMP prediction methods, none of them focused on DL methods. In this review, we aim to survey the latest AMP prediction methods achieved by DL approaches. First, the biology background of AMP is introduced, then various feature encoding methods used to represent the features of peptide sequences are presented. We explain the most popular DL techniques and highlight the recent works based on them to classify AMPs and design novel peptide sequences. Finally, we discuss the limitations and challenges of AMP prediction.

KeywordAntimicrobial Peptide Classification Deep Learning Machine Learning Medicine Regression Therapeutic Peptide
DOI10.3390/antibiotics11101451
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaInfectious Diseases ; Pharmacology & Pharmacy
WOS SubjectInfectious Diseases ; Pharmacology & Pharmacy
WOS IDWOS:000872032300001
Scopus ID2-s2.0-85140486633
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.PAMI Research Group, Department of Computer and Information Science, University of Macau, Taipa, Macao
2.Faculty of Applied Sciences, Macao Polytechnic University, Macao
3.Institute of Science and Environment, University of Saint Joseph, Estr. Marginal da Ilha Verde, Macao
4.NLP2CT Lab, Department of Computer and Information Science, University of Macau, Taipa, Macao
5.School of Pharmaceutical Sciences, Universiti Sains Malaysia, Pulau Pinang, 11800, Malaysia
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Yan, Jielu,Cai, Jianxiu,Zhang, Bob,et al. Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning[J]. Antibiotics,2022,11(10).
APA Yan, Jielu,Cai, Jianxiu,Zhang, Bob,Wang, Yapeng,Wong, Derek F.,&Siu, Shirley W.I..(2022).Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning.Antibiotics,11(10).
MLA Yan, Jielu,et al."Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning".Antibiotics 11.10(2022).
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