Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.
Open accessAbstract
This article provides an overview of methods for reliability assessment of quantitative structure-activity relationship (QSAR) models in the context of regulatory acceptance of human health and environmental QSARs. Useful diagnostic tools and data analytical approaches are highlighted and exemplified. Particular emphasis is given to the question of how to define the applicability borders of a QSAR and how to estimate parameter and prediction uncertainty. The article ends with a discussion regarding QSAR acceptability criteria. This discussion contains a list of recommended acceptability criteria, and we give reference values for important QSAR performance statistics. Finally, we emphasize that rigorous and independent validation of QSARs is an essential step toward their regulatory acceptance and implementation.
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- Authors
- Eriksson, L., Jaworska, J., Worth, A. P., Cronin, M. T. D., McDowell, R. M., & Gramatica, P.
- Affiliations
- Umetrics, Umeå, Sweden. lennart.eriksson@umetrics.com
- Venue
- Environmental Health Perspectives
- Publisher
- American Chemical Society (ACS)
- Published
- 2003-08-01
- Volume
- 111
- Issue
- 10
- Pages
- 1361-1375
- ISSN
- 0091-6765 (ISSN-L)
- 1552-9924
- PMC
- PMC1241620
- OpenAlex
- W2086190989
- Cited by
- 1,325
- References
- 100
- Citations by year
- 2026: 50 · 2025: 50 · 2024: 51 · 2023: 54 · 2022: 55 · 2021: 61 · 2020: 88 · 2019: 64 · 2018: 66 · 2017: 76 · 2016: 65 · 2015: 63 · 2014: 68 · 2013: 60 · 2012: 62
- FWCI
- 35.83
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Environmental Health Perspectives (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Liverpool John Moores University (acceptedVersion)
- IrInSubria (University of Insubria) (submittedVersion)
- Joint Research Centre (European Commission) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Environmental Toxicology and Ecotoxicology
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- uncertainty estimation, applicability domain, regulatory acceptance, QSAR validation, forecast uncertainty, parameter uncertainty
- MeSH terms
- Environmental Exposure, Europe, Humans, Models, Theoretical, Pattern Recognition, Automated, Regression Analysis, United States, Reproducibility of Results, Quantitative Structure-Activity Relationship, Government Regulation
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