<scp>AI</scp> Methods for Antimicrobial Peptides: Progress and Challenges

2025/01/01 by Carlos A. Brizuela, Gary Liu, Jonathan M. Stokes +1 · 1 voice · 3 citations
Immunology and Microbiology · Biochemistry, Genetics and Molecular Biology · #Antimicrobial Peptides and Activities #vaccines and immunoinformatics approaches #Biochemical and Structural Characterization

paper · pdf · doi:10.1111/1751-7915.70072

openalex publication_date 2025/01/01 · openalex created_date 2025/01/05 · openalex updated_date 2026/07/31

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.

Citations

• Amphipathic, α-helical antimicrobial peptides
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• Deep-learning-enabled antibiotic discovery through molecular de-extinction

Cited by

• Structure–Antimicrobial Activity Relationships of Recombinant Host Defence Peptides Against Drug‐Resistant Bacteria
• A Novel Chitin‐Based Purification System Using <scp>GAL1</scp> Fusion Tags: Enhancing Recombinant Protein Production While Retaining Biological Activity
• Why Static SAR Fails for Antimicrobial Peptides From an Ensemble and Free‐Energy Landscape Perspective?

Discussions

• #MicroSky: HOT in MBT 2025 AI is transforming antimicrobial-peptide discovery—deep learning, large protein language models, graph neural networks and structure-guided design now drive major advances. [bsky, 2 points, 0 comments]

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