How Well Can We Predict Mass Spectra from Structures? Benchmarking Competitive Fragmentation Modeling for Metabolite Identification on Untrained Tandem Mass Spectra.

ABSTRACT: Competitive Fragmentation Modeling for Metabolite Identification (CFM-ID) is a machine learning tool to predict in silico tandem mass spectra (MS/MS) for known or suspected metabolites for which chemical reference standards are not available. As a machine learning tool, it relies on both an underlying statistical model and an explicit training set that encompasses experimental mass spectra for specific compounds. Such mass spectra depend on specific parameters such as collision energies, instrument types, and adducts which are accumulated in libraries. Yet, ultimately prediction tools that are meant to cover wide expanses of entities must be validated on cases that were not included in the initial training and testing sets. Hence, we here benchmarked the performance of CFM-ID 4.0 to correc

SUBMITTER: Bremer PL 

PROVIDER: S-EPMC11521105 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature