The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

ullrika at the uncertainty show

Ullrika Sahlin

Senior lecturer

ullrika at the uncertainty show

A Risk Assessment Perspective of Current Practice in Characterizing Uncertainties in QSAR Regression Predictions

Author

  • Ullrika Sahlin
  • Monika Filipsson
  • Tomas Oberg

Summary, in English

The European REACH legislation accepts the use of non-testing methods, such as QSARs, to inform chemical risk assessment. In this paper, we aim to initiate a discussion on the characterization of predictive uncertainty from QSAR regressions. For the purpose of decision making, we discuss applications from the perspective of applying QSARs to support probabilistic risk assessment. Predictive uncertainty is characterized by a wide variety of methods, ranging from pure expert judgement based on variability in experimental data, through data-driven statistical inference, to the use of probabilistic QSAR models. Model uncertainty is dealt with by assessing confidence in predictions and by building consensus models. The characterization of predictive uncertainty would benefit from a probabilistic formulation of QSAR models (e. g. generalized linear models, conditional density estimators or Bayesian models). This would allow predictive uncertainty to be quantified as probability distributions, such as Bayesian predictive posteriors, and likelihood-based methods to address model uncertainty. QSAR regression models with point estimates as output may be turned into a probabilistic framework without any loss of validity from a chemical point of view. A QSAR model for use in probabilistic risk assessment needs to be validated for its ability to make reliable predictions and to quantify associated uncertainty.

Publishing year

2011

Language

English

Pages

551-564

Publication/Series

Molecular Informatics

Volume

30

Issue

6-7

Document type

Journal article

Publisher

John Wiley & Sons Inc.

Topic

  • Earth and Related Environmental Sciences

Keywords

  • Decision making
  • Predictive uncertainty
  • Probabilistic risk assessment
  • REACH
  • Regression

Status

Published

ISBN/ISSN/Other

  • ISSN: 1868-1751