PathoMIC: A Benchmark for Cross-Species Antimicrobial Peptide Activity Prediction

Abstract

With activity against multidrug-resistant pathogens and mechanisms distinct from conventional antibiotics, antimicrobial peptides (AMPs) offer a promising approach to combating antibiotic-resistant infections. However, their potency varies substantially across pathogen species, making accurate prediction of the minimum inhibitory concentration (MIC) for specific peptide-pathogen pairs essential for prioritizing candidates before costly experimental validation. Existing predictors are trained mainly on a few well-represented pathogens and rarely exploit biological relationships across species, limiting their generalization to low-resource and unseen pathogens. We introduce PathoMIC, the largest and most pathogen-diverse unified dataset for quantitative antimicrobial peptide activity prediction, containing 74,751 experimentally reported MIC measurements across 424 pathogen species. PathoMIC integrates peptide sequences, standardized MIC values, pathogen descriptions, and taxonomic relationships to facilitate knowledge transfer across related species. We establish few-shot and zero-shot cross-species evaluation protocols and develop a knowledge-enhanced framework that leverages pathogen descriptions and taxonomy. The framework yields substantial improvements for low-resource species with limited supervision, while gains for entirely unseen species remain modest, highlighting the difficulty of zero-shot cross-species MIC prediction. PathoMIC provides a standardized foundation for cross-species activity modeling and pathogen-specific virtual screening. Code is available at https://anonymous.4open.science/r/PathoMIC-546D/.