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Related Concept Videos

Uncertainty: Overview00:59

Uncertainty: Overview

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.
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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 particular...
2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)01:19

2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)

Heteronuclear single-quantum correlation spectroscopy (HSQC) is a 2D NMR technique that reveals one-bond correlations between hydrogen and a heteronucleus. The HSQC experiment is similar to the heteronuclear correlation experiment (HETCOR) but is more sensitive. In the HSQC spectrum, the proton chemical shift is plotted on the horizontal F2 axis, while the 13C chemical shift is plotted on the vertical F1 axis. The corresponding proton and 13C spectra are also shown. The HSQC contour plot does...
The Uncertainty Principle04:08

The Uncertainty Principle

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 mathematically...
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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 't,' or...

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Related Experiment Video

Updated: May 12, 2026

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
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In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

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Uncertainty in QSAR predictions.

Ullrika Sahlin1

  • 1Linnaeus University, School of Natural Sciences, Kalmar, Sweden. Ulrika.Sahlin@cec.lu.s

Alternatives to Laboratory Animals : ATLA
|April 26, 2013
PubMed
Summary

Uncertainty in quantitative structure-activity relationship (QSAR) predictions is crucial for chemical safety assessments. This study provides a framework to define, assess, and communicate QSAR prediction uncertainty, enhancing regulatory decision-making.

Area of Science:

  • Computational chemistry
  • Toxicology
  • Regulatory science

Background:

  • Quantitative structure-activity relationships (QSARs) are vital for chemical safety assessment under regulations like REACH.
  • Integrating QSAR predictions into decision-making requires clear communication of prediction uncertainty.
  • QSAR predictions inherently possess additional uncertainty compared to experimental data.

Purpose of the Study:

  • To establish a common understanding for defining, characterizing, assessing, and evaluating uncertainty in QSAR predictions.
  • To facilitate the successful integration of QSARs into chemical safety assessment.
  • To provide a framework for distinguishing between quantitative and qualitative uncertainty in QSAR predictions.

Main Methods:

  • Distinguishing quantitative uncertainty (predictive distribution) from qualitative uncertainty (confidence in model prediction).

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  • Assessing quantitative predictive distributions using supervised learning algorithms, QSAR data, probability models, and statistical inference principles.
  • Incorporating assessment of predictive error and reliability into the "unambiguous algorithm" framework.

    • Main Results:

    • A framework is presented to differentiate and assess quantitative (probabilistic) and qualitative (confidence-based) uncertainty in QSAR predictions.
    • The proposed methods allow for the assessment of predictive distributions based on learning algorithms and data.
    • The study outlines how to integrate predictive error and reliability assessments into established regulatory principles.

    Conclusions:

    • A clear framework for QSAR uncertainty is essential for reliable chemical safety assessments.
    • The proposed approach enhances the integration of QSARs into regulatory frameworks like REACH.
    • Addressing both quantitative and qualitative uncertainty improves the trustworthiness of QSAR-driven decisions.