# Competitive Fragmentation Modeling of ESI-MS/MS spectra for putative metabolite identification

arXiv: https://arxiv.org/abs/1312.0264 · HTML: https://mlsanity.com/paper/1312.0264/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2013.

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

- [DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra](https://mlsanity.com/paper/2502.09571/) (2025)
- [Prefix-Tree Decoding for Predicting Mass Spectra from Molecules](https://mlsanity.com/paper/2303.06470/) (2023)
- [Efficiently predicting high resolution mass spectra with graph neural networks](https://mlsanity.com/paper/2301.11419/) (2023)
- [FraGNNet: A Deep Probabilistic Model for Tandem Mass Spectrum Prediction](https://mlsanity.com/paper/2404.02360/) (2024)
- [MassFormer: Tandem Mass Spectrum Prediction for Small Molecules using Graph Transformers](https://mlsanity.com/paper/2111.04824/) (2021)
- [Neural Graph Matching Improves Retrieval Augmented Generation in Molecular Machine Learning](https://mlsanity.com/paper/2502.17874/) (2025)
- [Unlocking High-Fidelity Molecular Generation from Mass Spectra via Dual-Stream Line Graph Diffusion](https://mlsanity.com/paper/2605.07048/) (2026)
- [GLACIER: Rethinking Mass Spectrum Prediction as an Object Detection Problem](https://mlsanity.com/paper/2606.29161/) (2026)
- [S3C-LLM: Skill-Code Guided Agentic Language Models for Spectrum-to-Structure Elucidation](https://mlsanity.com/paper/2608.30910/) (2026)

## Abstract (arXiv)

Electrospray tandem mass spectrometry (ESI-MS/MS) is commonly used in high throughput metabolomics. One of the key obstacles to the effective use of this technology is the difficulty in interpreting measured spectra to accurately and efficiently identify metabolites. Traditional methods for automated metabolite identification compare the target MS or MS/MS spectrum to the spectra in a reference database, ranking candidates based on the closeness of the match. However the limited coverage of available databases has led to an interest in computational methods for predicting reference MS/MS spectra from chemical structures. This work proposes a probabilistic generative model for the MS/MS fragmentation process, which we call Competitive Fragmentation Modeling (CFM), and a machine learning approach for learning parameters for this model from MS/MS data. We show that CFM can be used in both a MS/MS spectrum prediction task (ie, predicting the mass spectrum from a chemical structure), and in a putative metabolite identification task (ranking possible structures for a target MS/MS spectrum). In the MS/MS spectrum prediction task, CFM shows significantly improved performance when compared to a full enumeration of all peaks corresponding to substructures of the molecule. In the metabolite identification task, CFM obtains substantially better rankings for the correct candidate than existing methods (MetFrag and FingerID) on tripeptide and metabolite data, when querying PubChem or KEGG for candidate structures of similar mass.