mlsanityData to 14 Oct 2026

FraGNNet: A Deep Probabilistic Model for Tandem Mass Spectrum Prediction

arXiv 2404.02360 ↗First posted 2 Apr 2024cs.LG q-bio.BMcited by 4

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Abstract (arXiv)

Compound identification from tandem mass spectrometry (MS/MS) data is a critical step in the analysis of complex mixtures. Typical solutions for the MS/MS spectrum to compound (MS2C) problem involve comparing the unknown spectrum against a library of known spectrum-molecule pairs, an approach that is limited by incomplete library coverage. Compound to MS/MS spectrum (C2MS) models can improve retrieval rates by augmenting real libraries with predicted MS/MS spectra. Unfortunately, many existing C2MS models suffer from problems with mass accuracy, generalization, or interpretability. We develop a new probabilistic method for C2MS prediction, FraGNNet, that can efficiently and accurately simulate MS/MS spectra with high mass accuracy. Our approach formulates the C2MS problem as learning a distribution over molecule fragments. FraGNNet achieves state-of-the-art performance in terms of prediction error and surpasses existing C2MS models as a tool for retrieval-based MS2C.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 16 of its 69 references that are in the map. First posted on arXiv 2 Apr 2024.

Builds on

16 of 69 references.

References · 16

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2018How Powerful are Graph Neural Networks?cited by 4,393
  3. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  4. 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,200
  5. 2018Relational inductive biases, deep learning, and graph networkscited by 1,360
  6. 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,051
  7. 2021Do Transformers Really Perform Bad for Graph Representation?cited by 320
  8. 2020Robust Optimization as Data Augmentation for Large-scale Graphscited by 58
  9. 2018Rapid Prediction of Electron-Ionization Mass Spectrometry using Neural Networkscited by 28
  10. 2023Prefix-Tree Decoding for Predicting Mass Spectra from Moleculescited by 16
  11. 2023Efficiently predicting high resolution mass spectra with graph neural networkscited by 13
  12. 2020Using Graph Neural Networks for Mass Spectrometry Predictioncited by 12
4 more
  1. 2013Competitive Fragmentation Modeling of ESI-MS/MS spectra for putative metabolite identificationcited by 9
  2. 2022Ensemble Spectral Prediction (ESP) Model for Metabolite Annotationcited by 8
  3. 2023Mass Spectra Prediction with Structural Motif-based Graph Neural Networkscited by 5
  4. 2021MassFormer: Tandem Mass Spectrum Prediction for Small Molecules using Graph Transformerscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2024MassSpecGym: A benchmark for the discovery and identification of moleculescited by 38
  2. 2025DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectracited by 32
  3. 2025Neural Graph Matching Improves Retrieval Augmented Generation in Molecular Machine Learningcited by 2
  4. 2026Transferable Mass Spectrum Prediction via Reference-Guided Test-time Specializationcited by 0