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此摘要是机器生成的。

药物发现失败了90%的候选人. 这项研究引入了一个使用AI/ML和化学相似性的计算框架,通过识别批准的小分子药物的非目标相互作用来预测药物重定向机会.

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科学领域:

  • 计算化学是一种计算化学.
  • 药物的发现和开发.
  • 药理学 药理学是指药理学的学科.

背景情况:

  • 由于毒性或疗效问题,小分子药物发现面临很高的磨损率 (~90%).
  • 经批准的药物与多个目标 (平均6-11个) 相互作用,表明了重定向的潜力.
  • 利用非目标相互作用可以为现有化合物发现新的治疗应用.

研究的目的:

  • 开发和验证用于小分子药物再利用的计算框架.
  • 为了确定FDA批准的药物的新型非目标相互作用.
  • 基于预测的药物标相互作用,探索潜在的新疗法应用.

主要方法:

  • 综合人工智能/机器学习 (AI/ML) 和化学相似性方法.
  • 采用了八种不同的目标预测方法,包括三种机器学习模型.
  • 分析了2766种FDA批准药物和跨物种转录组学数据的数据集.

主要成果:

  • 在2013年对2766种药物的27371种蛋白质标中确定了27371种非标相互作用.
  • 发现了150,620种与数据集中的药物结构相似的化合物.
  • 在实验室中确认了63% (17,283) 的预测的目标外相互作用,其中许多显示高亲和力 (IC50 < 100 nM 或 < 10 nM).
  • GPCRs,酶和激酶是预测相互作用的最常见的目标类.

结论:

  • 计算框架有效地预测了药物重定向的众多非目标相互作用.
  • 经过验证的相互作用和组织特异性表达模式为探索批准药物的新疗法用途提供了基础.
  • 这种方法提供了一个有前途的策略,以克服药物发现的磨损,并加快新治疗方法的开发.