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  3. 在小分子药物发现中用于机器学习的实用意义上的方法比较协议.
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  3. 在小分子药物发现中用于机器学习的实用意义上的方法比较协议.

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在小分子药物发现中用于机器学习的实用意义上的方法比较协议.

Jeremy R Ash1, Cas Wognum2,3, Raquel Rodríguez-Pérez4

  • 1Johnson & Johnson Innovative Medicine, Spring House, Pennsylvania 19477, United States.

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在PubMed 上查看摘要

概括
此摘要是机器生成的。

本研究介绍了在小分子药物发现中比较机器学习 (ML) 方法的指导方针. 严格的基准测试确保可靠的in silico模型用于财产预测,加速药物开发.

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科学领域:

  • 计算化学是一种计算化学.
  • 化学信息学 化学信息学
  • 机器学习是机器学习.
  • 发现药物的发现.

背景情况:

  • 机器学习 (ML) 模型预测分子性质,通过取代实验来帮助药物发现.
  • 目前的ML方法比较缺乏标准化,阻碍了可复制性和采用性.
  • 强有力的评估对于在小分子药物发现中做出高风险决策至关重要.

研究的目的:

  • 建议准则严格和适当的领域对ML方法进行小分子属性建模的严格和适当的比较.
  • 促进在药物发现中开发和采用可靠的ML工具.

主要方法:

  • 制定一套用于ML方法比较的指导方针.
  • 包含使用开源软件工具的注释示例.
  • 专注于统计严格的协议和适合领域的性能指标.

主要成果:

  • 在小分子属性预测中进行强大的ML基准测试的基础框架.
  • 旨在激励严格的技术并确保可复制性的指导方针.
  • 开源示例,以促进实际实施.

结论:

  • 标准化指南对于在小分子药物发现中推进ML至关重要.
  • 严格的基准测试确保了开发具有影响力和可靠的in silico工具.
  • 通过这些指导方针将促进信任并加速该领域的创新.