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Microorganisms play a fundamental role in vaccine development, gene therapy, and therapeutic production. Their biological properties are harnessed to advance medicine and public health. Beyond immunization, microorganisms contribute to gut health, antibiotic synthesis, and genetic disease treatment.Live Attenuated and Inactivated VaccinesLive attenuated vaccines, such as the measles, mumps, and rubella (MMR) vaccine, utilize weakened forms of pathogens to closely resemble natural infections.
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文章:使用机器学习来发现抗生素

Cesar de la Fuente-Nunez1,2,3,4, James J Collins5,6,7

  • 1University of Pennsylvania, Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, Philadelphia, Pennsylvania 19104, USA.

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机器学习和人工智能加速了新抗生素的发现,以对抗抗菌素耐药性,这是一个主要的全球健康威胁. 这些先进的计算方法为传统的药物发现方法提供了更快,更有效的替代方案.

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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) 是一个关键的全球健康威胁,被世界卫生组织确定为首要的公共卫生问题.
  • 耐药性病原体的兴起可能导致后抗生素时代的风险,使常见的感染具有潜在的致命性.
  • 传统的抗生素发现方法缓慢,昂贵,对快速发展的耐药性不足.

研究的目的:

  • 讨论机器学习 (ML) 和人工智能 (AI) 在加速抗生素发现中的作用.
  • 探索ML / AI在打击抗菌素耐药性的潜力和挑战.
  • 推测这个领域的未来演变,包括物理学方面的贡献.

主要方法:

  • 审查现有的文献和关于ML/AI在抗生素发现中的应用的见解.
  • 分析ML和AI如何加快潜在抗生素候选者的识别.
  • 考虑跨学科的贡献,特别是来自物理学界的贡献.

主要成果:

  • ML和AI显著减少了与识别新型抗生素候选者的时间和成本.
  • 这些技术为传统的,较慢的发现管道提供了变革性的替代方案.
  • 整合ML/AI有望为更强大的反抗抗药物危机作出更强有力的反应.

结论:

  • 在加速寻找新抗生素以应对抗菌素耐药性的全球挑战方面,ML和AI是至关重要的.
  • 该领域带来了重大机遇和挑战,具有未来进步和跨学科合作的潜力.
  • 计算方法的持续创新,包括基于物理学的方法,对于未来抗生素发现的成功至关重要.