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The Uncertainty Principle04:08

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Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
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Explicit memories, also known as declarative memories, are consciously remembered, recalled, and reported. Studying for a chemistry exam involves material that will become part of explicit memory. There are two types of explicit memory: episodic and semantic.
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显式适用性域计算可以帮助确定不确定性估计何时不太可靠.

Zied Hosni1, Valerie J Gillet1, Richard L Marchese Robinson2

  • 1Information School, University of Sheffield, The Wave, 2 Whitham Road, Sheffield S10 2AH, U.K.

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概括
此摘要是机器生成的。

定量结构-活性关系 (QSAR) 模型的不确定性估计是更可靠的,当化合物在模型的适用性领域. 使用k-最近邻近方法 (nUNC) 的结构相似性计算有助于确定这些不确定性估计何时对外部数据不那么可靠.

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科学领域:

  • 计算化学的计算化学
  • 化学信息学 化学信息学
  • 机器学习在药物发现中的应用

背景情况:

  • 定量结构-活性关系 (QSAR) 模型对于预测化学化合物特性至关重要.
  • 在QSAR中不确定性估计对于可靠的预测至关重要,符合规范回归和Venn-ABERS是最先进的方法.
  • 当这些方法应用于不同于训练集的数据分布时,这些方法的性能会下降,特别是在非随机的分割中,如时间或集群验证.

研究的目的:

  • 调查明确适用性域计算是否可以提高QSAR模型不确定性估计的可靠性.
  • 确定结构相似性是否可以预测不确定性估计对域外分子的可靠性较低.
  • 为了评估k-最近邻近的有效性适用性域方法 (nUNC) 与不确定性估计方法结合.

主要方法:

  • 通过使用示例数据集,比较不同的适用性领域和不确定性估计方法.
  • 广泛研究了可应用性域状态对不确定性估计可靠性的影响,使用k-最近邻近 (nUNC) 方法.
  • 结合nUNC与Cross-Venn-ABERS预测器 (分类) 和聚合合规预测 (回归) 在各种公共和工业数据集上,专注于非随机的时间和集群分割.

主要成果:

  • 使用结构相似性的明确适用性域计算有效地识别出不确定性估计在域外预测中可靠性较低的情况.
  • 结合不确定性估计方法的nUNC方法,证明了区分可靠的 (域内) 和不太可靠的 (域外) 不确定性估计的能力.
  • 这些发现在多个公共和工业数据集中是一致的,包括时间分割,突出了实际适用性.

结论:

  • 将基于结构相似性的适用性领域评估与不确定性估计方法相结合,大大提高了QSAR预测的可靠性.
  • nUNC方法是确定不确定性估计可靠性的宝贵工具,特别是对于模型训练分布之外的分子.
  • 这种方法对于QSAR模型遇到各种化学空间的现实应用至关重要.