Consensus Modeling Strategies for Predicting Transthyretin Binding Affinity from Tox24 Challenge Data

Transthyretin (TTR)
is a key transporter of the thyroid
hormone
thyroxine, and chemicals that bind to TTR, displacing the hormone,
can disrupt the endocrine system, even at low concentrations. This
study evaluates computational modeling strategies developed during
the Tox24 Challenge, using a data set of 1512 compounds tested for
TTR binding affinity. Individual models from nine top-performing teams
were analyzed for performance and uncertainty using regression metrics
and applicability domains (AD). Consensus models were developed by
averaging predictions across these models, with and without consideration
of their ADs. While applying AD constraints in individual models generally
improved external prediction accuracy (at the expense of reduced chemical
space coverage), it had limited additional benefit for consensus models.
Results showed that consensus models outperformed individual models,
achieving a root-mean-square error (RMSE) of 19.8% on the test set,
compared to an average RMSE of 20.9% for the nine individual models.
Outliers consistently identified in several of these models indicate
potential experimental artifacts and/or activity cliffs, requiring
further investigation. Substructure importance analysis revealed that
models prioritized different chemical features, and consensus averaging
harmonized these divergent perspectives. These findings highlight
the value of consensus modeling in improving predictive performance
and addressing model limitations. Future work should focus on expanding
chemical space coverage and refining experimental data sets to support
public health protection.