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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
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使用边缘化图核进行可解释的分子性质预测.

Yan Xiang1, Yu-Hang Tang2, Guang Lin3

  • 1Department of Biomedical Engineering, Duke University, Durham, North Carolina 27705, United States.

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|July 28, 2023
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概括
此摘要是机器生成的。

我们开发了与边缘化图核 (GPR-MGK) 进行高斯过程回归的新可解释性措施,以增强对分子机器学习的信任和理解. 与图形神经网络相比,GPR-MGK显示出优越的原子归属性.

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科学领域:

  • 计算化学是一种计算化学.
  • 机器学习 机器学习
  • 化学信息学 化学信息学

背景情况:

  • 边缘化的图核在分子机器学习中提供了强大的性能.
  • 目前的方法缺乏解释性,阻碍了信任,偏见检测和分子优化.
  • 可解释的人工智能对于推进分子科学至关重要.

研究的目的:

  • 引入和实施用于使用边缘化图核 (GPR-MGK) 的高斯过程回归的两个新的可解释性测量方法.
  • 量化训练数据和图形节点对预测的影响.
  • 提高GPR-MGK在分子性质预测中的可靠性和适用性.

主要方法:

  • 为GPR-MGK开发两个可解释性措施.
  • 培训数据对预测的贡献量化.
  • 对预测节点贡献的量化.
  • 与逻辑和毒理学数据集上的图形神经网络进行比较.
  • 在FreeSolv数据集上进行分子归因分析.

主要成果:

  • GPR-MGK解释性测量成功应用于分子性质预测.
  • 与跨数据集的图形神经网络相比,GPR-MGK表现出普遍优越的原子归属.
  • 详细的分析揭示了训练分子如何影响预测,并解释了摩根指纹的局限性.

结论:

  • 开发的可解释性措施显著提高了对GPR-MGK模型的理解.
  • GPR-MGK为图形神经网络提供了一个更易于解释的替代方案,用于分子性质预测.
  • 这项工作代表了分子机器学习的可解释边缘化图核方法的关键进步.