下一代抗真菌的发现:人工智能和omics技术的协同作用
Reihaneh Seiad Ahmadnezhad1, Masoomeh Shams-Ghahfarokhi2, Fatemehsadat Jamzivar1
1Department of Mycology, Pasteur Institute of Iran, Tehran, 1316943551, Iran.
World journal of microbiology & biotechnology
|November 18, 2025
概括
人工智能 (AI) 加快了抗真菌 (AFPs) 的发现,以对抗抗药性. 人工智能与奥米克和基因编辑相结合,增强了临床使用的AFP开发.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 药理学 药理学是指药理学的学科.
背景情况:
- 对真菌感染和抗真菌耐药性的日益关注,需要新的治疗策略.
- 抗真菌 (AFP) 是具有低抗性潜力的有效抗真菌药物.
- 识别AFPs的传统方法耗时,昂贵且低效.
研究的目的:
- 审查人工智能 (AI) 在克服抗真菌 (AFP) 开发方面的挑战中的作用.
- 突出人工智能驱动的在识别和设计新型AFP方面的进步.
- 讨论改善AFP有效性,安全性和临床应用的交付策略.
主要方法:
- 机器学习 (ML),深度学习 (DL) 和自然语言处理 (NLP) 用于AFP预测和设计的应用.
- 整合omics技术用于挖掘抗微生物 (AMP) 基因和生物合成基因集群 (BGCs).
- 利用像CRISPR-Cas9这样的基因编辑技术,在微生物中进行可扩展的AFP生产.
主要成果:
- 人工智能模型在预测抗真菌活性方面表现出更高的精度和准确性.
- 人工智能有助于开发具有增强功能和理想特征的AFP.
- 像转移学习,可解释AI (XAI) 和遗传算法等先进技术提高了AI模型的性能.
结论:
- 人工智能对于弥合AFP开发的差距至关重要,从发现到临床阶段.
- 将人工智能与奥米克和基因编辑相结合,为新的AFP发现和生产提供了强大的方法.
- 进一步的临床前和临床研究对于开发具有高效传递系统的安全,有效的AFPs至关重要.
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