Machine learning for precision prediction of antimicrobial peptide activity and spectrum.

Machine learning is reshaping antimicrobial peptide research by enabling precision-guided prediction and design, addressing the bottleneck of accurately forecasting potency and spectrum for clinical translation.

• Why it matters: Antibiotic resistance is accelerating, and AMPs are promising, but predicting antimicrobial potency and activity spectrum remains a major barrier to clinical use.
• What they did: This Review summarizes AMP mechanisms, evaluates AMP databases for machine-learning readiness, and reviews peptide representation learning plus frameworks for quantitative minimum inhibitory concentration prediction and strain-specific spectrum profiling.
• The result: The field is moving toward integrated generative-predictive pipelines, interpretable models, and closed-loop experimental validation to develop potent, selective, clinically viable antimicrobial therapeutics.

AI summary of the abstract · may contain errors · not medical advice · read the paper.