ArticleNature communications2025
FIORA: Local neighborhood-based prediction of compound mass spectra from single fragmentation events.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Enhanced Environmental PFAS Characterization Using a Virtual High-Resolution Mass Spectral Library Generated by Transfer Learning-Based Neural Network.Environmental science & technology · 2026Article
- Comprehensive Curation and Harmonization of Small-Molecule MS/MS Libraries in Spectraverse.Analytical chemistry · 2026Article
- FLARE: Fine-grained Learning for Alignment of spectra-molecule REpresentation Enhances Metabolite Annotation.bioRxiv : the preprint server for biology · 2026Article
- Simulating Tandem Mass Spectra for Small Molecules using a General-Purpose Large-Language Model.bioRxiv : the preprint server for biology · 2025Article
- General Intelligence-based Fragmentation (GIF): A framework for peak-labeled spectra simulation.ArXiv · 2025Article
- mineMS2: annotation of spectral libraries with exact fragmentation patterns.Journal of cheminformatics · 2025Article
- FIORA: Local neighborhood-based prediction of compound mass spectra from single fragmentation events.Nature communications · 2025Article
- Knowledge-based in silico fragmentation and annotation of mass spectra for natural products with MassKG.Computational and structural biotechnology journal · 2024Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Non-targeted metabolomics holds great promise for advancing precision medicine and biomarker discovery. However, identifying compounds from tandem mass spectra remains a challenging task due to the incomplete nature of spectral reference libraries. Augmenting these libraries with simulated mass spectra can provide the necessary references to resolve unmatched spectra, but generating high-quality data is difficult. In this study, we present FIORA, an open-source graph neural network designed to simulate tandem mass spectra. Our main contribution lies in utilizing the molecular neighborhood of bonds to learn breaking patterns and derive fragment ion probabilities. FIORA not only surpasses state-of-the-art fragmentation algorithms, ICEBERG and CFM-ID, in prediction quality, but also facilitates the prediction of additional features, such as retention time and collision cross section. Utilizing GPU acceleration, FIORA enables rapid validation of putative compound annotations and large-scale expansion of spectral reference libraries with high-quality predictions.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.