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佩加索斯:在细胞透性循环中解锁极性,使用基于大规模并行生物测试构建的AI模型.

Cole Baker1, Francis A Acquah1, Lakshmi G Chivukula1

  • 11910, Boston, Massachusetts 02210, United States.

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概括

人工智能 (AI) 和一种新型测试改善了口腔治疗的循环设计. 人工智能模型PEGASUS增强了细胞膜透性和水溶性预测,用于药物开发.

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科学领域:

  • 药用化学 医学化学
  • 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:

  • 循环在口服生物可用性方面显示出治疗前景,但在细胞膜透性和溶解性方面面临挑战.
  • 目前用于循环优化的人工智能 (AI) 方法受到稀缺,稀疏和疏水偏差的生物数据的限制.

研究的目的:

  • 开发一种多式人工智能模型 (PEGASUS),用于预测循环细胞膜的透性,并指导口服生物可用疗法的设计.
  • 通过集成高通量代理测试和计算模拟来克服数据限制.

主要方法:

  • 开发了PEGASUS,一种多式人工智能模型,集成了一种高通量代理生物测试 (1910 PPA),产生数十亿个循环.
  • 利用依赖溶剂的计算模拟来增强人工智能模型训练数据.
  • 根据极性和电荷,衍生了基于极性和电荷的高水溶性细胞透性循环的设计规则,模仿FDA批准的药物.

主要成果:

  • 在预测循环的细胞膜透性方面,PEGASUS取得了最先进的性能.
  • 该研究为增强型循环候选药物的设计原则提供了信息.
  • 成功设计了具有提高透性的新型循环,结合了多个极性或可电离的碎片.

结论:

  • 佩加索斯提供了一种强大的AI驱动方法,以克服循环药物设计的局限性.
  • 开发的方法使得能够创建具有良好的溶解性概况的细胞透性循环.
  • 这项工作促进了口服生物可用的循环治疗药物的开发.