Molecular networking, conformal predictions and revised fingerprint-based models for discovering endocrine disruptors in mixtures.

Prioritizing high-risk features is a key step to reduce workload in non-targeted screening (NTS) when identifying environmental contaminants. Machine learning models from the MS2Tox toolbox have shown promise for feature prioritization, but rely heavily on the accuracy of molecular formulas and fingerprint features provided by SIRIUS + CSI:FingerID. In this study, we introduce and evaluate two new approaches-molecular networking (MN) and conformal predictions-to discover unidentified compounds potentially posing endocrine-disrupting activity based on tandem mass spectral similarity. Furthermore, we revised the previously published MS2Tox models, leveraging molecular fingerprints for seven Tox21 Data Challenge endpoints. The fingerprint-based MS2Tox models achieved the lowest false positive rate, 0.35, at 90% recall on the test set, while MN and CP yielded 0.82 and 0.68, respectively. In a case study of transformation products and persistent chemicals in wastewater, these three approaches prioritized 29 features in influent and effluent samples as potentially associated with AhR agonism among 189 LC/HRMS features corresponding to transformation products and persistent chemicals. All candidate structures for prioritized features showed scaffolds related to AhR binding affinity. Three features were identified on level 1, showcasing potential in using combined feature prioritization strategies.