Application and prospect of artificial intelligence in antimicrobial peptides screening

XUE Feng; FENG Shuo; LI Jing

doi:10.11665/j.issn.1000-5048.2023030901

Journal of China Pharmaceutical University > 2023 > 54(3): 314-322. > DOI: 10.11665/j.issn.1000-5048.2023030901

XUE Feng, FENG Shuo, LI Jing. Application and prospect of artificial intelligence in antimicrobial peptides screening[J]. Journal of China Pharmaceutical University, 2023, 54(3): 314-322. DOI: 10.11665/j.issn.1000-5048.2023030901

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Application and prospect of artificial intelligence in antimicrobial peptides screening

1.
School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009
2.
Ministry of Education Key Laboratory of Drug Quality Control and Pharmacovigilance, China Pharmaceutical University, Nanjing 210009, China

Funds: This study was supported by the National Natural Science Foundation of China (No.32170062); and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (No.3322200020)

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Received Date: March 08, 2023
Revised Date: June 11, 2023

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Abstract

Abstract

Antimicrobial peptides (AMPs) are a class of small molecule peptides with broad-spectrum antimicrobial activity.Their unique antimicrobial mechanism can effectively treat infectious diseases, with rare drug resistance.However, though AMPs with antimicrobial activity can be screened by traditional methods, the whole process is complicated.The artificial intelligence (AI) screening method is faster and more convenient, with great potential in exploring new natural antimicrobial peptides.In this paper, strategies related to AMPs screening by AI were summarized and compared, including data sources applied to model training, artificial intelligence machine model and omics data applied to model screening of novel antimicrobial peptides.The application prospects and advantages were reviewed, in hope of providing new ideas for identification, research and development of antimicrobial peptides.
- artificial intelligence,
- machine learning,
- deep learning,
- antimicrobial peptides

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References (62)

References

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