Li Yanjian, Qiao Yue, Ma Yuanyuan, Xue Peng, Ding Chen
College of Life and Health Sciences, Northeastern University, Shenyang, China.
School of Public Health, Nantong University, Nantong, China.
Front Cell Infect Microbiol. 2025 May 21;15:1610743. doi: 10.3389/fcimb.2025.1610743. eCollection 2025.
The application of artificial intelligence (AI) in fungal drug development offers innovative strategies to address the escalating threat of fungal infections and the challenge of antifungal resistance. This review evaluates the current landscape of fungal infections, highlights the limitations of existing antifungal therapies, and examines the transformative potential of AI in drug discovery and development. We specifically focus on how AI can enhance the identification of new antifungal agents and improve therapeutic strategies. Despite numerous opportunities for advancement, significant challenges remain, particularly regarding data quality, regulatory frameworks, and the complexities associated with the drug development process. This review aims to provide insights into recent advancements in AI technologies, their implications for the future of fungal drug development, and the necessary research directions to effectively leverage AI for improved patient outcomes.
人工智能（AI）在抗真菌药物研发中的应用为应对不断升级的真菌感染威胁和抗真菌耐药性挑战提供了创新策略。本综述评估了真菌感染的现状，强调了现有抗真菌治疗方法的局限性，并探讨了AI在药物发现与开发中的变革潜力。我们特别关注AI如何增强新型抗真菌药物的识别能力并改进治疗策略。尽管有诸多进步机会，但仍存在重大挑战，尤其是在数据质量、监管框架以及与药物开发过程相关的复杂性方面。本综述旨在深入了解AI技术的最新进展、其对未来抗真菌药物研发的影响，以及有效利用AI改善患者预后所需的研究方向。
