ArticleMetabolomics : Official journal of the Metabolomic Society2020
MetFID: artificial neural network-based compound fingerprint prediction for metabolite annotation.
Article in Metabolomics : Official journal of the Metabolomic Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- From Target-Nontarget to Nontarget Screening: A Review on Screening Methods for Organic Pollutants Based on High-Resolution Mass Spectrometry.Environment & health (Washington, D.C.) · 2026Review
- Nature meets machine: the AI renaissance in natural product drug discovery.Natural products and bioprospecting · 2026Review
- Tracking the Metabolites of Health and Disease Using Artificial Intelligence.Diseases (Basel, Switzerland) · 2026Review
- Comprehensive Curation and Harmonization of Small-Molecule MS/MS Libraries in Spectraverse.Analytical chemistry · 2026Article
- ChemEmbed: a deep learning framework for metabolite identification using enhanced MS/MS data and multidimensional molecular embeddings.Briefings in bioinformatics · 2026Article
- DeePFAS: Deep-Learning-Enabled Rapid Annotation of PFAS: Enhancing Nontargeted Screening through Spectral Encoding and Latent Space Analysis.Environmental science & technology · 2025Article
- Recent Advances in Implementation of Machine Learning for Environmental Nontarget Identification.Environment & health (Washington, D.C.) · 2025Review
- Refining EI-MS library search results through atomic-level insights.Communications chemistry · 2025Article
- An approach of molecular-fingerprint prediction implementing a GAT.RSC advances · 2025Article
- A Comparative Study of Network-Based Machine Learning Approaches for Binary Classification in Metabolomics.Metabolites · 2025Article
- Article
- Artificial intelligence: the human response to approach the complexity of big data in biology.GigaScience · 2025Review
- Reproducible MS/MS library cleaning pipeline in matchms.Journal of cheminformatics · 2024Article
- MIST-CF: Chemical Formula Inference from Tandem Mass Spectra.Journal of chemical information and modeling · 2024Article
- Natural Products Dereplication: Databases and Analytical Methods.Progress in the chemistry of organic natural products · 2024Review
- Unlocking Potentially Therapeutic Phytochemicals in Capadulla (Metabolites · 2023Article
- DeepSAT: Learning Molecular Structures from Nuclear Magnetic Resonance Data.Journal of cheminformatics · 2023Article
- Deep Learning Based Metabolite Annotation.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2023Article
- Good practices and recommendations for using and benchmarking computational metabolomics metabolite annotation tools.Metabolomics : Official journal of the Metabolomic Society · 2022Review
- Multi-Task Neural Networks and Molecular Fingerprints to Enhance Compound Identification from LC-MS/MS Data.Molecules (Basel, Switzerland) · 2022Article
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4 authors.
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Abstract
introductionMetabolite annotation is a critical and challenging step in mass spectrometry-based metabolomic profiling. In a typical untargeted MS/MS-based metabolomic study, experimental MS/MS spectra are matched against those in spectral libraries for metabolite annotation. Yet, existing spectral libraries comprise merely a marginal percentage of known compounds.
objectiveThe objective is to develop a method that helps rank putative metabolite IDs for analytes whose reference MS/MS spectra are not present in spectral libraries.
methodsWe introduce MetFID, which uses an artificial neural network (ANN) trained for predicting molecular fingerprints based on experimental MS/MS data. To narrow the search space, MetFID retrieves candidates from metabolite databases using molecular formula or m/z value of the precursor ions of the analytes. The candidate whose fingerprint is most analogous to the predicted fingerprint is used for metabolite annotation. A comprehensive evaluation was performed by training MetFID using MS/MS spectra from the MoNA repository and NIST library and by testing with structure-disjoint MS/MS spectra from the NIST library, the CASMI 2016 dataset, and in-house MS/MS data from a cancer biomarker discovery study.
resultsWe observed that training separate models for distinct ranges of collision energies enhanced model performance compared to a single model that covers a wide range of collision energies. Using MetaboQuest to retrieve candidates, MetFID prioritized the correct putative ID in the first place rank for about 50% of the testing cases. Through the independent testing dataset, we demonstrated that MetFID has the potential to improve the accuracy of ranking putative metabolite IDs by more than 5% compared to other tools such as ChemDistiller, CSI:FingerID, and MetFrag.
conclusionMetFID offers a promising opportunity to enhance the accuracy of metabolite annotation by using ANN for molecular fingerprint prediction.
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