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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

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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

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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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Related Experiment Video

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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Uncertainty Quantification in Molecular Machine Learning for Property Predictions under Data Shifts.

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
Summary
This summary is machine-generated.

Machine learning (ML) models predict drug properties, but quantifying prediction uncertainty is key. Combining data and model-based uncertainty metrics with error models significantly improves reliability in molecular property prediction.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Machine learning applications

Background:

  • Machine learning (ML) models are vital for predicting compound properties in drug discovery.
  • Accurate prediction requires quantifying the uncertainty (UQ) of ML model outputs.
  • Existing UQ methods lack consistent superior performance across diverse datasets.

Purpose of the Study:

  • To benchmark various uncertainty quantification (UQ) strategies for ML-based prediction of absorption, distribution, metabolism, and excretion (ADME) properties.
  • To evaluate UQ method performance under data shifts using the UNIQUE framework.
  • To identify robust UQ approaches for reliable molecular property prediction.

Main Methods:

  • Comprehensive benchmarking of UQ strategies using in-house and public datasets.
  • Application of the UNIQUE (UNcertaInty QUantification bEnchmarking) framework.
  • Evaluation of UQ performance under various data shift scenarios.

Main Results:

  • Data-based (e.g., chemical distance) and model-based (e.g., prediction variance) UQ metrics capture complementary uncertainty aspects.
  • Combining diverse UQ metrics through error models, which predict ML model error, yields superior uncertainty estimates.
  • Error models demonstrate robustness and high-quality uncertainty estimation even with data shifts.

Conclusions:

  • Combining diverse UQ metrics and error modeling offers a promising strategy to enhance reliability in molecular property prediction.
  • Standardized evaluation setups and assessment under data shifts are crucial for future UQ method development.
  • This work provides a foundation for advancing UQ in cheminformatics and drug discovery.