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

Year: 2026

Type: dataset

Abstract: Predicted points of departure for reproductive/development and general non-cancer toxicity with 95% confidence intervals for 134,014 globally marketed chemicals predicted with "uncertainty-aware" machine learning models using random-forest based conformal prediction models.

Source: Figshare

DOI: 10.6084/m9.figshare.28482329

Authors: von Borries, Kerstin Johanna Felicitas, Beckwith, Katie V, Chiu, Weihsueh A, Goodman, Jonathan, Jolliet, Olivier, Fantke, Peter

Institution: Technical University of Denmark

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W7124996737

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