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从集体智能中利用药用化学直觉

Pierre Llompart1,2, Kwame Amaning1, Marc Bianciotto1

  • 1Molecular Design Sciences, Integrated Drug Discovery, Sanofi, 1 Imp. Des Ateliers, Vitry-sur-Seine 94400, France.

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概括

集体智能增强了ADMET终点的药物发现决策,在大多数领域都超过了人工智能. 结合人类和人工智能提供了一种协同方法,以改善项目成果.

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科学领域:

  • 药物的发现和开发.
  • 计算化学是一种计算化学.
  • 药品化学 药品化学 是一个

背景情况:

  • 药物发现中的决策往往依赖于个人的药物化学直觉.
  • 从群体努力中产生的集体智能,在复杂的问题解决方面表现有前途.
  • 集体智能应用于药物发现,特别是优化,仍然未被充分探索.

研究的目的:

  • 探索集体智能在药物发现中应用的优化.
  • 为了比较一个集体智能代理与人工智能模型的性能.
  • 评估人类和人工智能在药物发现决策中的协同作用.

主要方法:

  • 匿名调查了92名来自赛诺菲的研究人员,他们对ADMET相关问题有着不同的专业知识.
  • 根据研究人员的反开发了一个集体情报代理.
  • 将集体情报特工的表现与各种终点的人工智能模型进行了比较.

主要成果:

  • 与个人判断相比,集体智能显著改善了优化ADMET (吸收,分布,新陈代谢,分泌,毒性) 终点的决策.
  • 在大多数终点上,集体智能代理的表现优于人工智能模型,除了hERG抑制.
  • 在集体人类智能和人工智能之间观察到一种互补的关系.

结论:

  • 集体智能提供了一种有价值的工具,用于增强药物发现率优化中的决策.
  • 结合人类和人工智能的协同方法具有改善药物发现项目决策的巨大潜力.
  • 进一步研究将集体人类智能与人工智能集成,可以加速药物发现过程.