Harnessing artificial intelligence for antimicrobial discovery and optimization.

Authors

Ashley E Clements, Maureen R Fieldhouse, Allison S Walker

Published in

Current opinion in microbiology. Volume 91. Pages 102724. Epub Feb 25, 2026.

Abstract

The rise of antimicrobial-resistant pathogens has outpaced the traditional methods of drug discovery and development, emphasizing a need for new and innovative approaches to identifying novel antibiotics. Artificial intelligence (AI) poses new opportunities to overcome the challenges in traditional drug discovery by accelerating the identification, design, and optimization of bioactive small molecules and antimicrobial peptides. AI-driven genome mining allows for the identification and prioritization of biosynthetic gene clusters, while advanced AI models facilitate molecular property prediction, predicted binding interactions, and novel structure design. This review explores the advancements that AI has enabled in antimicrobial discovery and design, as well as its current limitations.

PMID:
41747630
Bibliographic data and abstract were imported from PubMed on 16 Sep 2026.

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