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在AstraZeneca使用预测性AI建模来增强DMTA.

Gian Marco Ghiandoni1, Emma Evertsson2, David J Riley1

  • 1Augmented DMTA Platform, R&D IT, AstraZeneca, The Discovery Centre (DISC), Francis Crick Avenue, Cambridge CB2 0AA, UK.

Drug discovery today
|March 9, 2024
PubMed
概括
此摘要是机器生成的。

人工智能和云计算简化了药物发现. 阿斯特拉泽内卡的预测洞察平台 (PIP) 加快了设计-制造-测试-分析周期,减少了识别可行的候选药物的代时间.

关键词:
人工智能的人工智能是人工智能.云计算是云计算中的一个.发现药物的发现.机器学习是机器学习.

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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.
  • 制药科学 制药科学

背景情况:

  • 传统的设计-制造-测试-分析 (DMTA) 循环是代的,可能需要多个循环来确定可行的候选药物.
  • 人工智能 (AI) 和云计算方面的进步有可能优化和加快药物发现过程.

研究的目的:

  • 介绍预测洞察平台 (PIP),这是一个在AstraZeneca开发的新型云原生建模平台.
  • 在DMTA框架内讨论PIP的影响,架构,集成和使用.
  • 提供对人工智能驱动药物发现未来的见解.

主要方法:

  • 开发一个云原生建模平台 (PIP).
  • 将PIP集成到设计-制造-测试-分析 (DMTA) 工作流中.
  • 分析PIP对DMTA循环每个阶段的影响.

主要成果:

  • 在DMTA循环的各个阶段,PIP提高了效率.
  • 该平台的架构和集成促进了无的数据流和分析.
  • 药物候选试剂 (PIP) 表明,它有可能显著减少用于候选药物识别所需的循环数.

结论:

  • 预测洞察平台 (PIP) 代表了人工智能驱动药物发现的重大进步.
  • 在DMTA循环中PIP的应用加速了可行的候选药物的识别.
  • 该平台为制药研发的未来轨迹提供了有价值的见解.