Interpretable machine learning classifies β-lactamase subclasses and prioritizes boundary variants for antimicrobial resistance surveillance

Introduction

Antimicrobial resistance is a major global health threat, and β-lactamase-mediated inactivation of β-lactam antibiotics remains a leading cause of treatment failure. Rapid identification of emerging β-lactamase variants and improved understanding of their functional diversity are therefore essential for resistance surveillance and therapeutic development.