Artificial intelligence in antibiotic discovery: Applications, challenges, and future outlook

Abstract

The rise of antimicrobial resistance has outpaced the discovery of antibiotics, creating a pressing global health crisis. Artificial intelligence (AI) offers tools to explore chemical and biological space more efficiently than traditional methods. Here, we review the use of AI in antibiotic research. We outline machine-learning models that have been applied to screen and optimize known compounds, including small molecules and peptides. We also summarize modern generative models leveraged to design antibiotic candidates. We cover approaches such as protein language models for advanced sequence and structural analysis, graph neural networks for modeling complex molecular interactions, and gene..