Machine Learning Predicts Antimicrobial Resistance from Genomic Data across ESKAPEE Pathogens

Antimicrobial resistance (AMR) is a mounting global crisis, fueled by the rapid emergence of multidrug-resistant bacteria. Among the most concerning culprits are the ESKAPEE bacteria—Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterobacter spp., and Escherichia coli—which are leading causes of hospital-acquired infections worldwide. In this study, we developed and validated machine learning models for predicting antimicrobial resistance phenotypes directly from genomic data. We assembled a robust dataset of 18,916 ESKAPEE genome assemblies, each paired with its corresponding antibiogram, covering susceptibility results for 40 different antibiotics. Using this data, we trained Random Forest and Extreme Gradient Boosting (XGBoost) models for each antibiotic separately, which consistently demonstrated excellent predictive performance, achieving over 90% recall and F1 score for almost all pathogen–antibiotic combinations. To maximize the utility and accessibility of our findings, we developed an interactive web platform (https://dianalab.e-ce.uth.gr/amrpredictor/) that allows users to explore prediction outcomes and identify the most informative genomic features driving resistance using Shap values. Furthermore, we rigorously validated our approach in a clinical setting. We applied our prediction pipeline to metagenomic sequencing data obtained from 36 blood culture-positive ESKAPEE samples. This real-world evaluation revealed a strong concordance between our predicted resistance profiles and conventional phenotypic results. Importantly, this metagenomic dataset also serves as a valuable, independent benchmark for future research in developing and evaluating AMR prediction models across ESKAPEE pathogens. Our work underscores the transformative potential of integrating genomics and machine learning to provide accurate, interpretable, and clinically actionable predictions for combating antimicrobial resistance.