Toxic Alerts of Endocrine Disruption Revealed by Explainable Artificial Intelligence.

ABSTRACT: The local interpretable model-agnostic explanation method was used to unveil substructures (toxic alerts) that cause endocrine disruption in chemical compounds using machine learning models. The random forest classifier was applied to build explainable models with the TOX21 data sets after data curation. Using these models applied to the EDC and EDKB-FDA data sets, the substructures that cause endocrine disruption in chemical compounds were unveiled, providing stable, more specific, and consistent explanations, which are essential for trust and acceptance of the findings, mainly due to the difficulty of finding relevant experimental evidence for different receptors (androgen, estrogen, aryl hydrocarbon, aromatase, and peroxisome proliferator-activated receptors). This approach is significa

SUBMITTER: Rosa LCS 

PROVIDER: S-EPMC11934200 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature