ArticleMetabolites2019
CFM-ID 3.0: Significantly Improved ESI-MS/MS Prediction and Compound Identification.
Article in Metabolites, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 103 papers.
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Who cites it
103 citing papers in PubMed.
- Effects of changing from a diet with saturated fat to a diet with n-6 polyunsaturated fat on the serum metabolome in relation to cardiovascular disease risk factors.European journal of nutrition · 2022Trial
- Hybrid Deep Learning Model for EI-MS Spectra Prediction.International journal of molecular sciences · 2026Article
- Tandem Mass Spectral Databases and Their Use in Non-Targeted Metabolomics.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Integrated Transcriptomic and Metabolomic Analysis Reveals Metabolic Heterosis in Hybrid Tea Plants (Genes · 2025Article
- ForMileS: A Python Open-Source Program to Generate Molecular Structures for Tandem Mass Spectrometry Fragment Ions.ACS omega · 2025Article
- Leveraging open cheminformatics tools for non-targeted metabolomics analysis of C. elegans: a workflow comparison and application to strains related to xenobiotic metabolism and neurodegeneration.Analytical and bioanalytical chemistry · 2025Article
- Large-scale generation of in silico based spectral libraries to annotate dark chemical space features in non-target analysis.Analytical and bioanalytical chemistry · 2025Article
- Metabolomic and chemometric profiling of crimson grapes during drying and its impact on antidiabetic activity.NPJ science of food · 2025Article
- Simplistic Software for Analyzing Mass Spectra and a Mixed Experimental-Theoretical Database for Identifying Poisonous and Explosive Substances.Journal of computational chemistry · 2025Article
- Evaluation of Violacein Metabolic Stability and Metabolite Identification in Human, Mouse, and Rat Liver Microsomes.Pharmaceutics · 2025Article
- Prediction of Fragmentation Pathway of Natural Products, Antibiotics, and Pesticides by ChemFrag.Journal of mass spectrometry : JMS · 2025Article
- Evaluating Metabolic Signatures in the Serum of South Korean Patients with Humidifier Disinfectant-Associated Lung Injury Identified through Untargeted Metabolomics.Environmental health perspectives · 2025Article
- Optical control of sphingolipid biosynthesis using photoswitchable sphingosines.Journal of lipid research · 2025Article
- Optical control of sphingolipid biosynthesis using photoswitchable sphingosines.bioRxiv : the preprint server for biology · 2024Article
- Deep learning prediction of electrospray ionization tandem mass spectra of chemically derived molecules.Nature communications · 2024Article
- Navigating common pitfalls in metabolite identification and metabolomics bioinformatics.Metabolomics : Official journal of the Metabolomic Society · 2024Review
- Article
- Molecular Responses of Anti-VEGF Therapy in Neovascular Age-Related Macular Degeneration: Integrative Insights From Multi-Omics and Clinical Imaging.Investigative ophthalmology & visual science · 2024Article
- GCMS-ID: a webserver for identifying compounds from gas chromatography mass spectrometry experiments.Nucleic acids research · 2024Article
- Machine learning-enhanced molecular network reveals global exposure to hundreds of unknown PFAS.Science advances · 2024Article
43 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Metabolite identification for untargeted metabolomics is often hampered by the lack of experimentally collected reference spectra from tandem mass spectrometry (MS/MS). To circumvent this problem, Competitive Fragmentation Modeling-ID (CFM-ID) was developed to accurately predict electrospray ionization-MS/MS (ESI-MS/MS) spectra from chemical structures and to aid in compound identification via MS/MS spectral matching. While earlier versions of CFM-ID performed very well, CFM-ID's performance for predicting the MS/MS spectra of certain classes of compounds, including many lipids, was quite poor. Furthermore, CFM-ID's compound identification capabilities were limited because it did not use experimentally available MS/MS spectra nor did it exploit metadata in its spectral matching algorithm. Here, we describe significant improvements to CFM-ID's performance and speed. These include (1) the implementation of a rule-based fragmentation approach for lipid MS/MS spectral prediction, which greatly improves the speed and accuracy of CFM-ID; (2) the inclusion of experimental MS/MS spectra and other metadata to enhance CFM-ID's compound identification abilities; (3) the development of new scoring functions that improves CFM-ID's accuracy by 21.1%; and (4) the implementation of a chemical classification algorithm that correctly classifies unknown chemicals (based on their MS/MS spectra) in >80% of the cases. This improved version called CFM-ID 3.0 is freely available as a web server. Its source code is also accessible online.
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Registered trials
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