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相关概念视频

Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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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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Uncertainty: Overview00:59

Uncertainty: Overview

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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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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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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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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The Uncertainty Principle04:08

The Uncertainty Principle

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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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Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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相关实验视频

Updated: Jan 17, 2026

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
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Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

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分子机器学习中的不确定性量化用于数据转移下的属性预测.

Raquel Parrondo-Pizarro1,2, Jessica Lanini1, Raquel Rodríguez-Pérez1

  • 1Novartis Biomedical Research, Novartis Campus, Basel 4002, Switzerland.

Journal of chemical information and modeling
|January 14, 2026
PubMed
概括

机器学习 (ML) 模型预测药物特性,但量化预测不确定性是关键. 将数据和基于模型的不确定性指标与错误模型相结合,可以显著提高分子性质预测的可靠性.

科学领域:

  • 计算化学是一种计算化学.
  • 药物发现 药物发现
  • 机器学习应用程序 机器学习应用程序

背景情况:

  • 机器学习 (ML) 模型对于预测药物发现中的化合物特性至关重要.
  • 准确的预测需要量化ML模型输出的不确定性 (UQ).
  • 现有的UQ方法在不同的数据集中缺乏一致的优异性能.

研究的目的:

  • 为了对各种不确定性量化 (UQ) 策略进行基准测试,以基于ML的吸收,分布,新陈代谢和分泌 (ADME) 属性的预测.
  • 使用UNIQUE框架在数据转移下评估UQ方法性能.
  • 确定可靠的UQ方法,以可靠地预测分子性质.

主要方法:

  • 使用内部和公共数据集对UQ策略进行全面的比较.
  • 应用UNIQUE (不确定的定量化与标记) 框架.
  • 在各种数据转移场景下评估UQ性能.

主要成果:

  • 基于数据 (例如化学距离) 和基于模型 (例如预测方差) 的UQ指标捕捉了互补的不确定性方面.
  • 通过预测ML模型错误的错误模型,将各种UQ指标结合起来,可以获得优异的不确定性估计.
  • 错误模型表现出稳定性和高质量的不确定性估计,即使数据转移.

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Last Updated: Jan 17, 2026

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Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
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Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

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A Simple, Robust, and High Throughput Single Molecule Flow Stretching Assay Implementation for Studying Transport of Molecules Along DNA
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A Simple, Robust, and High Throughput Single Molecule Flow Stretching Assay Implementation for Studying Transport of Molecules Along DNA

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结论:

  • 结合各种UQ指标和错误建模,提供了一个有希望的策略,以提高分子性质预测的可靠性.
  • 标准化的评估设置和数据转移下的评估对于未来的UQ方法开发至关重要.
  • 这项工作为推进化学信息学和药物发现领域的UQ提供了基础.