人工智能和机器学习在抗微生物发现,耐药性预测和精确治疗方面
Reema Singh1, Yu-Wei Lin2, Jinxin Zhao2
1Vaccine and Infectious Disease Organization, University of Saskatchewan, Saskatoon, SK, Canada.
International journal of antimicrobial agents
|January 11, 2026
概括
人工智能 (AI) 和机器学习 (ML) 加快了新抗微生物药物的发现,并改善了耐药性预测. 战略性人工智能部署对于打击耐药病原体至关重要.
科学领域:
- 微生物学 微生物学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 抗微生物药物耐药性 (AMR) 构成了严重的全球卫生危机.
- 传统的药物发现方法不足以应对这一挑战.
- 人工智能 (AI) 和机器学习 (ML) 提供了变革性的潜力.
研究的目的:
- 突出AI/ML在加速抗菌药物发现中的作用.
- 展示AI/ML在革命性抗性预测和诊断方面的能力.
- 强调投资,数据共享和合作的必要性.
主要方法:
- AI/ML用于新的药物设计和虚拟查.
- AI/ML用于识别新药标和机制.
- 整合基因组和表型数据来预测耐药性.
主要成果:
- 人工智能/ML显著加快了潜在的新型抗菌素的识别速度.
- 通过AI/ML,可以更准确地预测抗菌素耐药性.
- AI/ML促进了快速诊断和个性化的抗菌疗法.
结论:
- 人工智能和机器学习是打击抗微生物耐药性的重要工具.
- 克服数据稀缺性和模型可解释性等挑战是关键.
- 持续投资和全球跨学科合作对于成功至关重要.
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