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Advances in the Application of Deep Learning for Antimicrobial Peptide Screening
Review Article  ·  Published: 02 April 2026
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Agricultural Science and Food Processing
Volume 3, Issue 2, 2026: 49-72
Review Article Open Access

Advances in the Application of Deep Learning for Antimicrobial Peptide Screening

by
Yuting Fu Yuting Fu 1 * ORCID
1 School of Light Industry Science and Engineering, Beijing Technology and Business University, Beijing 100048, China
* Corresponding Author: Yuting Fu, [email protected]
Volume 3, Issue 2
Volume 3, Issue 2 2026
Received: 29 February 2026 Accepted: 30 March 2026 Published: 02 April 2026 CrossMark
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DOI: 10.62762/ASFP.2026.121905
ARK: ark:/57805/asfp.2026.121905

Article Information

Published in Agricultural Science and Food Processing
Volume/Issue Volume 3, Issue 2, 2026
Pages 49-72

Abstract

The problem of bacterial resistance to antibiotics is becoming more and more serious, and it is urgent to develop new antibacterial drugs to cope with this situation. Antimicrobial peptides (AMPs) are a group of natural peptides with advantages including broad-spectrum antibacterial activity and a low tendency to induce resistance, and have become attractive alternatives to antibiotics. However, their broad application is constrained by the inefficiency and high cost of conventional screening methods. Recently, deep learning (DL) has enabled more streamlined identification, design, and prediction of AMP activity through advanced data processing and pattern recognition. A few review articles have previously summarized the application of machine learning (ML) in the identification of antimicrobial peptides, but none of them have detailed the latest advancements in DL. In this review, the latest AMP classification and screening approaches achieved by DL techniques are surveyed. First, the biological background of AMPs is introduced, followed by current applications of antimicrobial peptides in industry and medical treatment. The most popular DL techniques are explained, and state-of-the-art research based on these techniques for classifying antimicrobial peptides and designing novel peptide sequences is highlighted. Finally, the limitations and challenges of AMP prediction are discussed.

Graphical Abstract

Advances in the Application of Deep Learning for Antimicrobial Peptide Screening

Keywords

deep learning antimicrobial peptides classification screening prediction drug resistance

Data Availability Statement

Not applicable.

Funding

This work was supported without any funding.

Conflicts of Interest

The author declares no conflicts of interest.

AI Use Statement

The author declares that no generative AI was used in the preparation of this manuscript.

Ethical Approval and Consent to Participate

Not applicable.

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APA Style
Fu, Y. (2026). Advances in the Application of Deep Learning for Antimicrobial Peptide Screening. Agricultural Science and Food Processing, 3(2), 49–72. https://doi.org/10.62762/ASFP.2026.121905
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TY  - JOUR
AU  - Fu, Yuting
PY  - 2026
DA  - 2026/04/02
TI  - Advances in the Application of Deep Learning for Antimicrobial Peptide Screening
JO  - Agricultural Science and Food Processing
T2  - Agricultural Science and Food Processing
JF  - Agricultural Science and Food Processing
VL  - 3
IS  - 2
SP  - 49
EP  - 72
DO  - 10.62762/ASFP.2026.121905
UR  - https://www.icck.org/article/abs/ASFP.2026.121905
KW  - deep learning
KW  - antimicrobial peptides
KW  - classification
KW  - screening
KW  - prediction
KW  - drug resistance
AB  - The problem of bacterial resistance to antibiotics is becoming more and more serious, and it is urgent to develop new antibacterial drugs to cope with this situation. Antimicrobial peptides (AMPs) are a group of natural peptides with advantages including broad-spectrum antibacterial activity and a low tendency to induce resistance, and have become attractive alternatives to antibiotics. However, their broad application is constrained by the inefficiency and high cost of conventional screening methods. Recently, deep learning (DL) has enabled more streamlined identification, design, and prediction of AMP activity through advanced data processing and pattern recognition. A few review articles have previously summarized the application of machine learning (ML) in the identification of antimicrobial peptides, but none of them have detailed the latest advancements in DL. In this review, the latest AMP classification and screening approaches achieved by DL techniques are surveyed. First, the biological background of AMPs is introduced, followed by current applications of antimicrobial peptides in industry and medical treatment. The most popular DL techniques are explained, and state-of-the-art research based on these techniques for classifying antimicrobial peptides and designing novel peptide sequences is highlighted. Finally, the limitations and challenges of AMP prediction are discussed.
SN  - 3066-1579
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
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@article{Fu2026Advances,
  author = {Yuting Fu},
  title = {Advances in the Application of Deep Learning for Antimicrobial Peptide Screening},
  journal = {Agricultural Science and Food Processing},
  year = {2026},
  volume = {3},
  number = {2},
  pages = {49-72},
  doi = {10.62762/ASFP.2026.121905},
  url = {https://www.icck.org/article/abs/ASFP.2026.121905},
  abstract = {The problem of bacterial resistance to antibiotics is becoming more and more serious, and it is urgent to develop new antibacterial drugs to cope with this situation. Antimicrobial peptides (AMPs) are a group of natural peptides with advantages including broad-spectrum antibacterial activity and a low tendency to induce resistance, and have become attractive alternatives to antibiotics. However, their broad application is constrained by the inefficiency and high cost of conventional screening methods. Recently, deep learning (DL) has enabled more streamlined identification, design, and prediction of AMP activity through advanced data processing and pattern recognition. A few review articles have previously summarized the application of machine learning (ML) in the identification of antimicrobial peptides, but none of them have detailed the latest advancements in DL. In this review, the latest AMP classification and screening approaches achieved by DL techniques are surveyed. First, the biological background of AMPs is introduced, followed by current applications of antimicrobial peptides in industry and medical treatment. The most popular DL techniques are explained, and state-of-the-art research based on these techniques for classifying antimicrobial peptides and designing novel peptide sequences is highlighted. Finally, the limitations and challenges of AMP prediction are discussed.},
  keywords = {deep learning, antimicrobial peptides, classification, screening, prediction, drug resistance},
  issn = {3066-1579},
  publisher = {Institute of Central Computation and Knowledge}
}

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CC BY Copyright © 2026 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
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