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

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 generative models for de novo generation of antimicrobial compounds. We discuss how these methods have accelerated hit identification in silico and sometimes in vitro and even in vivo , while also noting important challenges. Finally, we outline future directions, which could help AI fulfill its promise in discovering next-generation antibiotics.