Wu Pengfei, Li Wanwu, Zhang Wenlu, Li Shasha, Deng Bo, Xu Shanghui, Li Zhongjie
Microbial Pathogen and Anti-Infection Research Group, School of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Luoyang 471003, China.
Microorganisms. 2025 Aug 21;13(8):1960. doi: 10.3390/microorganisms13081960.
The escalating global threat of antimicrobial resistance (AMR) underscores the urgent need for innovative therapeutics. Bacteriophages (phages), natural bacterial predators, offer promising solutions, especially when harnessed through advances in artificial intelligence (AI). This review explores how AI-driven innovations are transforming phage biology, with an emphasis on three pivotal areas: (1) AI-enhanced structural prediction (e.g., AlphaFold); (2) deep learning functional annotation; (3) bioengineering strategies, including CRISPR-Cas. We further discuss applications extending to medical therapy, biosensing, agricultural biocontrol, and environmental remediation. Despite progress, critical challenges persist-including high false-positive rates, difficulties in modeling disordered protein regions, and biosafety concerns remain. Overcoming these requires experimental validation, robust computational frameworks, and global regulatory oversight. AI integration in phage research is accelerating the development of next-generation therapeutics to combat AMR and advance engineered living therapeutics.
全球抗菌药物耐药性（AMR）威胁的不断升级凸显了对创新疗法的迫切需求。噬菌体作为天然的细菌捕食者，提供了有前景的解决方案，特别是通过人工智能（AI）的进展来加以利用时。本综述探讨了人工智能驱动的创新如何正在改变噬菌体生物学，重点关注三个关键领域：（1）人工智能增强的结构预测（例如AlphaFold）；（2）深度学习功能注释；（3）生物工程策略，包括CRISPR-Cas。我们还讨论了其在医学治疗、生物传感、农业生物防治和环境修复等方面的应用。尽管取得了进展，但关键挑战依然存在，包括高假阳性率、对无序蛋白质区域进行建模的困难以及生物安全问题。克服这些问题需要实验验证、强大的计算框架以及全球监管监督。噬菌体研究中的人工智能整合正在加速下一代抗AMR疗法的开发，并推动工程化活体疗法的发展。
