Machine Learning-based Classification for the Prioritization of Potentially Hazardous Chemicals with Structural Alerts in Nontarget Screening.

ABSTRACT: Nontarget screening (NTS) with liquid chromatography high-resolution mass spectrometry (LC-HRMS) is commonly used to detect unknown organic micropollutants in the environment. One of the main challenges in NTS is the prioritization of relevant LC-HRMS features. A novel prioritization strategy based on structural alerts to select NTS features that correspond to potentially hazardous chemicals is presented here. This strategy leverages raw tandem mass spectra (MS²) and machine learning models to predict the probability that NTS features correspond to chemicals with structural alerts. The models were trained on fragments and neutral losses from the experimental MS² data. The feasibility of this approach is evaluated for two groups: aromatic amines and organophosphorus st

SUBMITTER: Meekel N 

PROVIDER: S-EPMC11924234 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature