# Fragmentation trees reloaded

arXiv: https://arxiv.org/abs/1412.1929 · HTML: https://mlsanity.com/paper/1412.1929/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2014.

## Built on by (most cited first, 9 of 9)

- [MassSpecGym: A benchmark for the discovery and identification of molecules](https://mlsanity.com/paper/2410.23326/) (2024)
- [DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra](https://mlsanity.com/paper/2502.09571/) (2025)
- [Unraveling Molecular Structure: A Multimodal Spectroscopic Dataset for Chemistry](https://mlsanity.com/paper/2407.17492/) (2024)
- [IR-Agent: Expert-Inspired LLM Agents for Structure Elucidation from Infrared Spectra](https://mlsanity.com/paper/2508.16112/) (2025)
- [Atomic Diffusion Models for Small Molecule Structure Elucidation from NMR Spectra](https://mlsanity.com/paper/2512.03127/) (2025)
- [FRIGID: Scaling Diffusion-Based Molecular Generation from Mass Spectra at Training and Inference Time](https://mlsanity.com/paper/2604.16648/) (2026)
- [MassFormer: Tandem Mass Spectrum Prediction for Small Molecules using Graph Transformers](https://mlsanity.com/paper/2111.04824/) (2021)
- [De novo molecular structure elucidation from mass spectra via flow matching](https://mlsanity.com/paper/2602.19912/) (2026)
- [When should we trust the annotation? Selective prediction for molecular structure retrieval from mass spectra](https://mlsanity.com/paper/2603.10950/) (2026)

## Abstract (arXiv)

Metabolites, small molecules that are involved in cellular reactions, provide a direct functional signature of cellular state. Untargeted metabolomics experiments usually relies on tandem mass spectrometry to identify the thousands of compounds in a biological sample. Today, the vast majority of metabolites remain unknown. Fragmentation trees have become a powerful tool for the interpretation of tandem mass spectrometry data of small molecules. These trees are found by combinatorial optimization, and aim at explaining the experimental data via fragmentation cascades. To obtain biochemically meaningful results requires an elaborate optimization function. We present a new scoring for computing fragmentation trees, transforming the combinatorial optimization into a maximum a posteriori estimator. We demonstrate the superiority of the new scoring for two tasks: Both for the de novo identification of molecular formulas of unknown compounds, and for searching a database for structurally similar compounds, our methods performs significantly better than the previous scoring, as well as other methods for this task. Our method can expedite the workflow for untargeted metabolomics, allowing researchers to investigate unknowns using automated computational methods.