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A Risk Assessment Perspective of Current Practice in Characterizing Uncertainties in QSAR Regression Predictions
Ullrika Sahlin1, Monika Filipsson2, Tomas Öberg2
1School of Natural Sciences, Linnaeus University, 391 82 Kalmar, Sweden. Ullrika.Sahlin@lnu.se.
Molecular Informatics
|July 29, 2016
Summary
This study discusses characterizing predictive uncertainty in Quantitative Structure-Activity Relationship (QSAR) models for chemical risk assessment. Probabilistic QSAR formulations are recommended for reliable uncertainty quantification in decision-making.
Area of Science:
- Computational chemistry
- Toxicology
- Regulatory science
Background:
- European REACH legislation permits non-testing methods like QSARs for chemical risk assessment.
- Characterizing predictive uncertainty is crucial for reliable QSAR applications in risk assessment.
- Existing methods for uncertainty characterization vary widely, from expert judgment to statistical inference.
Purpose of the Study:
- To initiate a discussion on characterizing predictive uncertainty in QSAR regressions.
- To explore the application of QSARs in probabilistic risk assessment for decision-making.
- To highlight the benefits of probabilistic QSAR formulations for quantifying uncertainty.
Main Methods:
- Reviewing various methods for characterizing predictive uncertainty.
- Discussing approaches to address model uncertainty, including confidence assessment and consensus modeling.
- Proposing probabilistic formulations for QSAR models (e.g., Bayesian models, conditional density estimators).
Main Results:
- Probabilistic QSAR models allow uncertainty quantification as probability distributions.
- Likelihood-based methods can effectively address model uncertainty.
- Point-estimate QSAR models can be converted to a probabilistic framework without loss of chemical validity.
Conclusions:
- Probabilistic QSAR formulations are beneficial for quantifying predictive uncertainty.
- Validated QSAR models must reliably predict and quantify associated uncertainty for probabilistic risk assessment.
- This approach supports informed decision-making in chemical risk assessment under regulations like REACH.
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