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相关概念视频

Uncertainty: Confidence Intervals00:54

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
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结合贝叶斯式和证据性不确定性量化,改善生物活性建模.

Bola Khalil1,2, Kajetan Schweighofer3, Natalia Dyubankova2

  • 1Computational Drug Discovery (CDD), Division of Medicinal Chemistry, Leiden University, 2333 CC Leiden, The Netherlands.

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|December 7, 2025
PubMed
概括
此摘要是机器生成的。

结合贝叶斯方法和证据学习的混合方法改善了药物发现的不确定性量化. 提出的证据模型 (EOE) 组合为可靠的生物活性建模提供了计算效率高和强大的解决方案.

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科学领域:

  • 计算化学和化学信息学
  • 机器学习在药物发现中的作用
  • 在预测建模中的不确定性量化.

背景情况:

  • 在药物发现中,可靠的计算建模需要强大的不确定性量化 (UQ).
  • 贝叶斯方法 (深层合奏,MC脱落) 和证据学习提供了独特的UQ能力和计算权衡.
  • 现有的方法在表达性和复杂的药物发现任务的计算需求方面存在局限性.

研究的目的:

  • 开发和评估混合的UQ方法,将贝叶斯方法和证据学习结合起来.
  • 将这些混合模型与药物发现数据集上的既定方法进行基准比较.
  • 评估用于生物活性预测的新型UQ策略的性能和计算效率.

主要方法:

  • 实现混合模型,将贝叶斯原则与证据学习相结合.
  • 基于Papyrus++数据集对生物活性终点 (xC50,Kx) 的基准测试,使用不同的数据分割.
  • 使用诸如根平均平方误差 (RMSE),连续排列概率得分 (CRPS) 和间隔得分等指标进行评估.

主要成果:

  • 证据模型 (EOE) 组合在所有测试的终点和分割策略中表现出卓越的性能.
  • EOE获得了最低的RMSE和领先的CRPS和间隔分数,超过了传统方法.
  • 在实用指标上,EOE与大型集团相匹配或超越,计算成本显著降低.

结论:

  • 混合UQ方法,特别是EOE,为生物活性建模提供了更准确和更有信息的不确定性.
  • 在药物发现中,EOE为不确定性意识的决策提供了一个计算上实用和强大的默认.
  • 这些发现倡导将证据和贝叶斯原则整合起来,以提高化学信息学中的UQ.