# Ensemble Spectral Prediction (ESP) Model for Metabolite Annotation

arXiv: https://arxiv.org/abs/2203.13783 · HTML: https://mlsanity.com/paper/2203.13783/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 35 references that are in the map.
- First posted on arXiv 25 Mar 2022.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Strategies for Pre-training Graph Neural Networks](https://mlsanity.com/paper/1905.12265/) (2019)
- [Rapid Prediction of Electron-Ionization Mass Spectrometry using Neural Networks](https://mlsanity.com/paper/1811.08545/) (2018)
- [Deriving Neural Architectures from Sequence and Graph Kernels](https://mlsanity.com/paper/1705.09037/) (2017)
- [Using Graph Neural Networks for Mass Spectrometry Prediction](https://mlsanity.com/paper/2010.04661/) (2020)

## Built on by (most cited first, 8 of 8)

- [MassSpecGym: A benchmark for the discovery and identification of molecules](https://mlsanity.com/paper/2410.23326/) (2024)
- [MADGEN: Mass-Spec attends to De Novo Molecular generation](https://mlsanity.com/paper/2501.01950/) (2025)
- [JESTR: Joint Embedding Space Technique for Ranking Candidate Molecules for the Annotation of Untargeted Metabolomics Data](https://mlsanity.com/paper/2411.14464/) (2024)
- [FraGNNet: A Deep Probabilistic Model for Tandem Mass Spectrum Prediction](https://mlsanity.com/paper/2404.02360/) (2024)
- [De novo molecular structure elucidation from mass spectra via flow matching](https://mlsanity.com/paper/2602.19912/) (2026)
- [MSAlign: Aligning Molecule and Mass Spectra representations for Metabolite Identification](https://mlsanity.com/paper/2605.19752/) (2026)
- [LSM-MS2: A Foundation Model Bridging Spectral Identification and Biological Interpretation](https://mlsanity.com/paper/2510.26715/) (2025)
- [General Intelligence-based Fragmentation (GIF): A framework for peak-labeled spectra simulation](https://mlsanity.com/paper/2511.09571/) (2025)

## Abstract (arXiv)

A key challenge in metabolomics is annotating measured spectra from a biological sample with chemical identities. Currently, only a small fraction of measurements can be assigned identities. Two complementary computational approaches have emerged to address the annotation problem: mapping candidate molecules to spectra, and mapping query spectra to molecular candidates. In essence, the candidate molecule with the spectrum that best explains the query spectrum is recommended as the target molecule. Despite candidate ranking being fundamental in both approaches, no prior works utilized rank learning tasks in determining the target molecule. We propose a novel machine learning model, Ensemble Spectral Prediction (ESP), for metabolite annotation. ESP takes advantage of prior neural network-based annotation models that utilize multilayer perceptron (MLP) networks and Graph Neural Networks (GNNs). Based on the ranking results of the MLP and GNN-based models, ESP learns a weighting for the outputs of MLP and GNN spectral predictors to generate a spectral prediction for a query molecule. Importantly, training data is stratified by molecular formula to provide candidate sets during model training. Further, baseline MLP and GNN models are enhanced by considering peak dependencies through multi-head attention mechanism and multi-tasking on spectral topic distributions. ESP improves average rank by 41% and 30% over the MLP and GNN baselines, respectively, demonstrating remarkable performance gain over state-of-the-art neural network approaches. We show that annotation performance, for ESP and other models, is a strong function of the number of molecules in the candidate set and their similarity to the target molecule.