Artificial Intelligence as a Multifaceted Toolkit for Antibacterial Drug Discovery against Resistance

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Abstract
How artificial intelligence (AI) can be rationally applied to antibacterial drug discovery and development to overcome antimicrobial resistance (AMR) remains insufficiently defined, particularly across different resistance mechanisms and translational stages. The global emergence and dissemination of AMR are compromising conventional antibacterial therapies, while slow antibiotic discovery, limited target diversity, and reliance on single-mechanism drug design further increase the risk of inadequate treatment options. With strengths in multi-omics integration, complex pattern recognition, and structure-guided molecular design, AI offers a promising approach to support next-generation antibacterial development. This review focuses on AI-related strategies for antimicrobial development against bacterial resistance and provides a systematic overview of advances in target identification, molecular design, activity prediction, and clinical translation. Rather than proposing a new theoretical framework, the review introduces a pragmatic synthesis tool for evaluating where AI adds value and where important limitations remain, considering application objectives, data properties, validation depth, biological interpretability, and translational feasibility. Major challenges are highlighted, including data heterogeneity, clonal bias, limited external validation, discrepancies between in vitro activity and in vivo therapeutic efficacy, and the absence of regulatory approval for any AI-discovered or AI-designed antibacterial agent. Correspondingly, future directions are delineated, including multimodal data integration, explainable AI methodologies, validation through synthetic biology, and coordinated international regulatory efforts. Overall, this review provides a structured overview of how AI can be integrated with rational antibacterial design while considering both its potential and current limitations for addressing multidrug-resistant pathogens.
Funding Information
  • National Natural Science Foundation of China (82373835,82574369)
  • Basic and Applied Basic Research Foundation of Guangdong Province (2024A1515140138, 2023A1515110417, 2023A1515140131)
  • Basic and Applied Basic Research Foundation of Guangdong Province (2020B1515120033)
  • Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province (pdjh2024a388)

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