Uncertainty-aware machine learning to predict non-cancer human toxicity for the global chemicals market.

ABSTRACT: Humans are exposed to thousands of chemicals, yet limited toxicity data hinder effective management of their impacts on human health. High-performing machine learning models hold potential for addressing this gap, but their uncharacterized prediction performance across the wider range of chemicals undermines confidence in their results. We develop uncertainty-aware models to predict reproductive/developmental and general non-cancer human toxicity effect doses. Our well-calibrated models provide uncertainty estimates aligned with observed prediction errors and chemical familiarity. We predict toxicity with 95% confidence intervals for >100,000 globally marketed chemicals and identify toxicity and uncertainty hotspots. These results can be applied to inform decisions aimed at reducing potent

SUBMITTER: von Borries K 

PROVIDER: S-EPMC12816579 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature