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Consensus Modeling Strategies for Predicting Transthyretin Binding Affinity from Tox24 Challenge Data.
Thalita Cirino1, Luis Pinto2, Mateusz Iwan3
1Molecular Biotechnology and Health Sciences Department, University of Turin, Turin 10126, Italy.
Chemical Research in Toxicology
|May 15, 2025
Summary
Consensus modeling improves predictions of chemical binding to transthyretin (TTR), a key thyroid hormone transporter. This approach enhances accuracy and identifies potential experimental issues, aiding in endocrine disruption assessment.
Area of Science:
- Computational toxicology
- Endocrine disruption
- Pharmacology
Background:
- Transthyretin (TTR) transports thyroid hormones, and chemicals binding to it can disrupt the endocrine system.
- Assessing TTR binding affinity is crucial for identifying potential endocrine disruptors.
Purpose of the Study:
- To evaluate computational modeling strategies for predicting TTR binding affinity.
- To assess the performance and uncertainty of individual and consensus models.
Main Methods:
- Analysis of 1512 compounds using regression metrics and applicability domains (AD).
- Development of consensus models by averaging predictions from nine top-performing individual models.
- Comparison of consensus models with and without AD constraints.
Main Results:
- Consensus models outperformed individual models, with a lower root-mean-square error (RMSE) of 19.8% on the test set.
- Applying AD constraints improved individual model accuracy but had limited impact on consensus models.
- Identified outliers suggest potential experimental artifacts or activity cliffs.
Conclusions:
- Consensus modeling enhances predictive performance and addresses limitations of individual computational models.
- Harmonizing divergent model perspectives through averaging improves reliability.
- Further research should expand chemical space coverage and refine experimental data.
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