整合机器学习和多目标药物设计来打击抗微生物药物耐药性:系统性审查
Nagmi Bano1, Salman Arafath Mohammed2,3, Khalid Raza1
1Computational Intelligence and Bioinformatics Lab., Department of Computer Science, Jamia Millia Islamia, New Delhi, India.
Journal of drug targeting
|November 13, 2024
概括
机器学习和多目标药物设计提供了新的策略来打击Enterobacteriaceae中的抗菌素耐药性 (AMR). 这些方法加速了新药和组合疗法的发现,这对于应对这一全球健康威胁至关重要.
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:
- 抗菌素耐药性 (AMR) 是一个关键的全球卫生挑战,多药耐药性 (MDR) 使治疗无效.
- 优先细菌菌株,包括肠杆菌 (例如大肠杆菌,沙门氏菌,克勒比氏菌),每年导致数百万人的死亡.
- 世界卫生组织已经确定AMR细菌是全球的主要威胁,需要采取紧急行动.
研究的目的:
- 探索机器学习 (ML) 和多目标药物设计 (MTDD) 在对抗Enterobacteriaceae中的AMR方面的潜力.
- 系统地审查当前的前景和局限性,将AI和计算技术整合到抗药性研究中.
- 确定创新策略,开发针对耐药细菌的有效治疗方法.
主要方法:
- 按照PRISMA指南进行系统的文献审查.
- 分析了PubMed和Scopus文学数据库中的数据.
- 机器学习算法和多目标药物设计原则的探索.
主要成果:
- 机器学习和人工智能使大数据集的分析能够识别新的药物标并预测耐药性机制.
- MTDD有助于优化药物分子和开发针对多种细菌途径的组合疗法.
- 这些综合方法在克服现有的抗性和减少新抗性的发展方面显示出有希望.
结论:
- 整合ML和MTDD对于推动对Enterobacteriaceae的AMR的斗争至关重要.
- 这些计算策略可以加速新型抗菌剂的发现和开发.
- 开发针对多种细菌生存途径的组合疗法是缓解耐药性的关键.
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