Menu

Javascript is not activated in your browser. This website needs javascript activated to work properly.
You are here

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

Author:
  • Ullrika Sahlin
  • Monika Filipsson
  • Tomas Oberg
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

Abstract 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.

Keywords

  • Earth and Related Environmental Sciences
  • Decision making
  • Predictive uncertainty
  • Probabilistic risk assessment
  • REACH
  • Regression

Other

Published
  • ISSN: 1868-1751
me in Lundagård
E-mail: ullrika.sahlin [at] cec.lu.se

Researcher

Centre for Environmental and Climate Research (CEC)

+46 46 222 68 31

+46 73 827 44 32

E-D340

50

Read more on the blog

The dawn of the new research group “UnEviL”

2017-04-13
Uncertainty and Evidence Lab is the name of a new research group at Lund University. The group is led by Ullrika Sahlin at the Centre of Env…

Bayes@Lund2017 20th April

2017-03-23
The program for Bayes@Lund2017 is now ready Follow us at #bayeslund17 on twitter We start in room MA4, Maths building Annex, Sölvegatan 20. …

Workshop on Bayesian Networks for risk assessment and decision making

2017-02-08
We had a successful workshop on Bayesian Networks in risk assessment and decision making in Lund March 28 and 29th, 2017. This workshop was …

Centre for Environmental and Climate Research, CEC

Sölvegatan 37
223 62 Lund, Sweden

Visiting address
The Ecology building, Sölvegatan 37, Lund