Machine Learning-Assisted False Positive Detection in Metabolite Identification Workflows.

ABSTRACT: Metabolite identification is a pivotal step in drug discovery and development, enabling the comprehensive analysis of drug-derived compounds within biological systems. However, the complexity of liquid chromatography-mass spectrometry data often results in numerous false positives, complicating the identification of true metabolites. This study introduces a machine-learning-based approach to improve the accuracy of false positive detection in metabolite identification workflows. By incorporating expert knowledge, we develop a feature set for metabolite-related chromatographic peaks that characterizes true and false positives with high accuracy, integrating data from mass spectra, chromatographic signals, and kinetic profiles. We validate this method via gradient boosting decision tree clas

SUBMITTER: Adalia R 

PROVIDER: S-EPMC12750405 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature