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Life sciences · Journal article
Microorganisms · October 15, 2026
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The escalating crisis of antimicrobial resistance (AMR) demands innovative therapeutic strategies beyond conventional antibiotics. Antimicrobial peptides (AMPs), particularly those originating from the human, animal, and environmental microbiome, represent a promising frontier within public health microbiology and microbial food safety. Recent advances in artificial intelligence (AI) and machine learning (ML) have revolutionized the discovery pipeline, enabling high-throughput mining of metagenomic data and de novo generation of novel peptide sequences. This perspective review synthesizes recent breakthroughs in AI-driven discovery of microbiome-originated AMPs, examines their preclinical efficacy against multidrug-resistant (MDR) pathogens under One Health and climate-sensitive frameworks and application in infectious disease treatment and discusses translational challenges including pharmacokinetics, toxicity, manufacturing scalability, and applications in food preservation and water safety. We argue that the convergence of microbiome science, deep learning, and synthetic biology positions AI-discovered AMPs as viable next-generation interventions against antibiotic resistance across human, animal, and environmental domains.