Machine learning for precision prediction of antimicrobial peptide activity and spectrum.
TL;DR
How recent advances in machine learning are reshaping AMP research is examined, driving a shift from large-scale discovery toward precision-guided prediction and design and emphasizing integrated generative-predictive pipelines, interpretable models, and closed-loop experimental validation as key enablers for the development of potent, selective, and clinically viable antimicrobial therapeutics.
Abstract
The accelerating global crisis of antibiotic resistance demands new therapeutic paradigms, and antimicrobial peptides (AMPs) have emerged as promising candidates owing to their broad activity and reduced propensity for resistance development. However, despite rapid progress in AMP discovery and generation, the accurate prediction of antimicrobial potency and activity spectrum remains a major bottleneck for clinical translation. In this Review, we examine how recent advances in machine learning are reshaping AMP research, driving a shift from large-scale discovery toward precision-guided prediction and design. We first summarize the molecular mechanisms underlying AMP function and critically assess existing AMP databases from the perspective of machine learning readiness, highlighting limitations in quantitative and spectrum-resolved annotations. We then review recent developments in peptide representation learning, describing how modern models encode sequence, structure, and dynamic features to capture antimicrobial activity. Building on this foundation, we discuss progress in de novo AMP design and emerging frameworks for quantitative minimum inhibitory concentration prediction and strain-specific spectrum profiling. Finally, we outline future directions for the field, emphasizing integrated generative-predictive pipelines, interpretable models, and closed-loop experimental validation as key enablers for the development of potent, selective, and clinically viable antimicrobial therapeutics.