Deep Learning-Based Molecular Fingerprint Prediction for Metabolite Annotation.

ABSTRACT: Background/Objectives: Liquid chromatography coupled with mass spectrometry (LC-MS) is a commonly used platform for many metabolomics studies. However, metabolite annotation has been a major bottleneck in these studies in part due to the limited publicly available spectral libraries, which consist of tandem mass spectrometry (MS/MS) data acquired from just a fraction of known compounds. Application of deep learning methods is increasingly reported as an alternative to spectral matching due to their ability to map complex relationships between molecular fingerprints and mass spectrometric measurements. The objectives of this study are to investigate deep learning methods for molecular fingerprint based on MS/MS spectra and to rank putative metabolite IDs according to similarity of th

SUBMITTER: Chau HYK 

PROVIDER: S-EPMC11857613 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature