Essay: Using Machine Learning for Antibiotic Discovery
César de la Fuente‐Núñez, James J. Collins
Abstract
Antimicrobial resistance is a critical global health challenge and one of the World Health Organization's top ten public health threats. The alarming rise of drug-resistant pathogens threatens to usher in a postantibiotic era where common infections could once again become fatal. Despite the urgency, traditional discovery methods are time-consuming, expensive, and insufficient to keep pace with rapidly evolving resistance. Recent advances in machine learning (ML) and artificial intelligence (AI) present a transformative alternative, enabling the rapid identification of potential candidate antibiotics in a fraction of the time required by conventional methods. In this Essay, we discuss how ML and AI significantly accelerate antibiotic discovery, drawing on previous works and insights in this emerging field. We also consider the promises and challenges of this emerging area and speculate on its evolution in the coming years, highlighting the potential contributions from the physics community. Part of a series of Essays in Physical Review Letters which concisely present author visions for the future of their field.