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整合基于机器学习的姿势采样与虚拟选的既定评分功能.

Thi Ngoc Lan Vu1,2,3, Hosein Fooladi1,2,3, Johannes Kirchmair1,2

  • 1Department of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria.

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概括
此摘要是机器生成的。

机器学习对接,DiffDock-L,为基于物理的虚拟选 (VS) 方法提供了一个有竞争力的替代方案. 将其与各种评分功能相结合,有望提高药物发现工作流程.

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科学领域:

  • 计算化学是一种计算化学.
  • 药物发现 药物发现
  • 在化学信息学中的机器学习.

背景情况:

  • 基于物理的对接是基于结构的虚拟选 (VS) 的传统方法.
  • 机器学习 (ML) 方法正在成为增强VS技术的强大工具.

研究的目的:

  • 将基于ML的姿势采样方法DiffDock-L集成到VS工作流中.
  • 为了评估DiffDock-L在与Vina,Gnina和RTMScore评分功能相结合时的性能.
  • 为了比较综合方法与传统的基于物理的对接方法,如AutoDock Vina.

主要方法:

  • 使用DefDock-L进行虚拟选中的姿势采样.
  • 集成的DiffDock-L与Vina,Gnina和RTMScore分数功能. 这是一个非常好的解决方案.
  • 使用交叉对接场景对 DUDE-Z 基准数据集的性能进行评估.

主要成果:

  • DiffDock-L展示了具有竞争力的虚拟选性能和质量抽样质量.
  • 基于ML的方法产生了物理可信和生物相关的姿势.
  • 发现得分函数的选择对虚拟选成功产生了重大影响.

结论:

  • DiffDock-L为VS提供了传统基于物理的对接算法的可行替代方案.
  • 机器学习方法的整合对推进虚拟查技术具有重大潜力.
  • 优化评分功能选择对于最大限度地提高基于ML的VS方法的有效性至关重要.