Machine Learning-Assisted False Positive Detection in Metabolite Identification Workflows
Adàlia, Ramon (Universitat Autònoma de Barcelona)
Cifuentes, Paula (Universitat Pompeu Fabra)
Liu, Joyce (Genentech)
Cheruzel, Lionel (Genentech)
Sanjuan, Gemma (Universitat Autònoma de Barcelona)
Margalef, Tomàs (Universitat Autònoma de Barcelona)
Zamora, Ismael (Lead Molecular Design, S.L.)

Fecha:	2025
Descripción:	9 pàg.
Resumen:	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 classifiers on both publicly available and proprietary "real-world" data sets, including small molecules and new modalities. Our findings demonstrate that machine learning-assisted techniques significantly reduce false positive identifications, thereby increasing the efficiency and accuracy of metabolite identification processes.
Ayudas:	Generalitat de Catalunya 2023/DI-00006 Generalitat de Catalunya 2023/DI-00002
Derechos:	Aquesta url de drets no existeix a la base de dades.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Publicado en:	Analytical chemistry, December 2025, ISSN 1520-6882

DOI: 10.1021/acs.analchem.5c02745
PMID: 41371620

9 p, 2.3 MB

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