Convolutional Neural Network-Based Compound Fingerprint Prediction for Metabolite Annotation.

ABSTRACT: Metabolite annotation has been a challenging issue especially in untargeted metabolomics studies by liquid chromatography coupled with mass spectrometry (LC-MS). This is in part due to the limitations of publicly available spectral libraries, which consist of tandem mass spectrometry (MS/MS) data acquired from just a fraction of known metabolites. Machine learning provides the opportunity to predict molecular fingerprints based on MS/MS data. The predicted molecular fingerprints can then be used to help rank putative metabolite IDs obtained by using either the precursor mass or the formula of the unknown metabolite. This method is particularly useful to help annotate metabolites whose corresponding MS/MS spectra are missing or cannot be matched with those in accessible spectral libraries.

SUBMITTER: Gao S 

PROVIDER: S-EPMC9316655 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature