AI Methods for Antimicrobial Peptides: Progress and Challenges
ABSTRACT Antimicrobial peptides (AMPs) are promising candidates to combat multidrug‐resistant pathogens. However, the high cost of extensive wet‐lab screening has made AI methods for identifying and designing AMPs increasingly important, with machine learning (ML) techniques playing a crucial role....
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Microbial Biotechnology |
| Online Access: | https://doi.org/10.1111/1751-7915.70072 |
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| author | Carlos A. Brizuela Gary Liu Jonathan M. Stokes Cesar de laFuente‐Nunez |
| author_facet | Carlos A. Brizuela Gary Liu Jonathan M. Stokes Cesar de laFuente‐Nunez |
| author_sort | Carlos A. Brizuela |
| collection | DOAJ |
| description | ABSTRACT Antimicrobial peptides (AMPs) are promising candidates to combat multidrug‐resistant pathogens. However, the high cost of extensive wet‐lab screening has made AI methods for identifying and designing AMPs increasingly important, with machine learning (ML) techniques playing a crucial role. AI approaches have recently revolutionised this field by accelerating the discovery of new peptides with anti‐infective activity, particularly in preclinical mouse models. Initially, classical ML approaches dominated the field, but recently there has been a shift towards deep learning (DL) models. Despite significant contributions, existing reviews have not thoroughly explored the potential of large language models (LLMs), graph neural networks (GNNs) and structure‐guided AMP discovery and design. This review aims to fill that gap by providing a comprehensive overview of the latest advancements, challenges and opportunities in using AI methods, with a particular emphasis on LLMs, GNNs and structure‐guided design. We discuss the limitations of current approaches and highlight the most relevant topics to address in the coming years for AMP discovery and design. |
| format | Article |
| id | doaj-art-7798f106a7784810b52cfbb0b039a798 |
| institution | Kabale University |
| issn | 1751-7915 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Microbial Biotechnology |
| spelling | doaj-art-7798f106a7784810b52cfbb0b039a7982025-01-31T06:26:35ZengWileyMicrobial Biotechnology1751-79152025-01-01181n/an/a10.1111/1751-7915.70072AI Methods for Antimicrobial Peptides: Progress and ChallengesCarlos A. Brizuela0Gary Liu1Jonathan M. Stokes2Cesar de laFuente‐Nunez3Department of Computer Science CICESE Research Center Ensenada MexicoDepartment of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, David Braley Centre for Antibiotic Discovery McMaster University Hamilton Ontario CanadaDepartment of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, David Braley Centre for Antibiotic Discovery McMaster University Hamilton Ontario CanadaMachine Biology Group, Department of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine University of Pennsylvania Philadelphia Pennsylvania USAABSTRACT Antimicrobial peptides (AMPs) are promising candidates to combat multidrug‐resistant pathogens. However, the high cost of extensive wet‐lab screening has made AI methods for identifying and designing AMPs increasingly important, with machine learning (ML) techniques playing a crucial role. AI approaches have recently revolutionised this field by accelerating the discovery of new peptides with anti‐infective activity, particularly in preclinical mouse models. Initially, classical ML approaches dominated the field, but recently there has been a shift towards deep learning (DL) models. Despite significant contributions, existing reviews have not thoroughly explored the potential of large language models (LLMs), graph neural networks (GNNs) and structure‐guided AMP discovery and design. This review aims to fill that gap by providing a comprehensive overview of the latest advancements, challenges and opportunities in using AI methods, with a particular emphasis on LLMs, GNNs and structure‐guided design. We discuss the limitations of current approaches and highlight the most relevant topics to address in the coming years for AMP discovery and design.https://doi.org/10.1111/1751-7915.70072 |
| spellingShingle | Carlos A. Brizuela Gary Liu Jonathan M. Stokes Cesar de laFuente‐Nunez AI Methods for Antimicrobial Peptides: Progress and Challenges Microbial Biotechnology |
| title | AI Methods for Antimicrobial Peptides: Progress and Challenges |
| title_full | AI Methods for Antimicrobial Peptides: Progress and Challenges |
| title_fullStr | AI Methods for Antimicrobial Peptides: Progress and Challenges |
| title_full_unstemmed | AI Methods for Antimicrobial Peptides: Progress and Challenges |
| title_short | AI Methods for Antimicrobial Peptides: Progress and Challenges |
| title_sort | ai methods for antimicrobial peptides progress and challenges |
| url | https://doi.org/10.1111/1751-7915.70072 |
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