# Using Graph Neural Networks for Mass Spectrometry Prediction

arXiv: https://arxiv.org/abs/2010.04661 · HTML: https://mlsanity.com/paper/2010.04661/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2020.

## Built on by (most cited first, 12 of 12)

- [MassSpecGym: A benchmark for the discovery and identification of molecules](https://mlsanity.com/paper/2410.23326/) (2024)
- [Prefix-Tree Decoding for Predicting Mass Spectra from Molecules](https://mlsanity.com/paper/2303.06470/) (2023)
- [MADGEN: Mass-Spec attends to De Novo Molecular generation](https://mlsanity.com/paper/2501.01950/) (2025)
- [Efficiently predicting high resolution mass spectra with graph neural networks](https://mlsanity.com/paper/2301.11419/) (2023)
- [Ensemble Spectral Prediction (ESP) Model for Metabolite Annotation](https://mlsanity.com/paper/2203.13783/) (2022)
- [JESTR: Joint Embedding Space Technique for Ranking Candidate Molecules for the Annotation of Untargeted Metabolomics Data](https://mlsanity.com/paper/2411.14464/) (2024)
- [Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond](https://mlsanity.com/paper/2502.09897/) (2025)
- [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)
- [SpecBridge: Bridging Mass Spectrometry and Molecular Representations via Cross-Modal Alignment](https://mlsanity.com/paper/2601.17204/) (2026)
- [GLACIER: Rethinking Mass Spectrum Prediction as an Object Detection Problem](https://mlsanity.com/paper/2606.29161/) (2026)

## Abstract (arXiv)

Detecting and quantifying products of cellular metabolism using Mass Spectrometry (MS) has already shown great promise in many biological and biomedical applications. The biggest challenge in metabolomics is annotation, where measured spectra are assigned chemical identities. Despite advances, current methods provide limited annotation for measured spectra. Here, we explore using graph neural networks (GNNs) to predict the spectra. The input to our model is a molecular graph. The model is trained and tested on the NIST 17 LC-MS dataset. We compare our results to NEIMS, a neural network model that utilizes molecular fingerprints as inputs. Our results show that GNN-based models offer higher performance than NEIMS. Importantly, we show that ranking results heavily depend on the candidate set size and on the similarity of the candidates to the target molecule, thus highlighting the need for consistent, well-characterized evaluation protocols for this domain.