Artificial intelligence in antimicrobial drug discovery: predictive and generative strategies

The slow pipeline of antimicrobial drug development stands in stark contrast to the continued expansion of microbial infections, which pose a persistent and major threat to global public health. Traditional discovery strategies, including natural product extraction, structural modification and high-throughput screening, are limited by low efficiency, high costs and slow innovation. Artificial intelligence (AI), particularly machine learning, deep learning, and natural language processing, is now reshaping drug discovery, bringing unprecedented speed and precision to the development of novel antimicrobial drugs.

This review summarizes recent advances and challenges in AI-driven strategies for discovering antimicrobial drugs against bacterial, fungal, and viral infections, covering major drug classes including small molecules, peptides, phages, and protein drugs. The article was based on literature retrieved through a structured, iterative search of major scientific databases (PubMed, Web of Science, Scopus), with a focus on studies published between 2020 and 2025.

The integration of AI-driven predictive and generative strategies will be the defining cornerstone of the next decade's 'autonomous discovery' paradigm for antimicrobial drug development, despite current enduring challenges including data bias, lack of standardized benchmarking frameworks, and clinical translational gaps.