# MassSpecGym: A benchmark for the discovery and identification of molecules

arXiv: https://arxiv.org/abs/2410.23326 · HTML: https://mlsanity.com/paper/2410.23326/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 83 references that are in the map.
- First posted on arXiv 30 Oct 2024.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval](https://mlsanity.com/paper/2205.13760/) (2022)
- [Rapid Prediction of Electron-Ionization Mass Spectrometry using Neural Networks](https://mlsanity.com/paper/1811.08545/) (2018)
- [Efficiently predicting high resolution mass spectra with graph neural networks](https://mlsanity.com/paper/2301.11419/) (2023)
- [Using Graph Neural Networks for Mass Spectrometry Prediction](https://mlsanity.com/paper/2010.04661/) (2020)
- [Visualization of Very Large High-Dimensional Data Sets as Minimum Spanning Trees](https://mlsanity.com/paper/1908.10410/) (2019)
- [Fragmentation trees reloaded](https://mlsanity.com/paper/1412.1929/) (2014)
- [Ensemble Spectral Prediction (ESP) Model for Metabolite Annotation](https://mlsanity.com/paper/2203.13783/) (2022)
- [FraGNNet: A Deep Probabilistic Model for Tandem Mass Spectrum Prediction](https://mlsanity.com/paper/2404.02360/) (2024)

## Built on by (most cited first, 30 of 38)

- [Probing the limitations of multimodal language models for chemistry and materials research](https://mlsanity.com/paper/2411.16955/) (2024)
- [MADGEN: Mass-Spec attends to De Novo Molecular generation](https://mlsanity.com/paper/2501.01950/) (2025)
- [MS-BART: Unified Modeling of Mass Spectra and Molecules for Structure Elucidation](https://mlsanity.com/paper/2510.20615/) (2025)
- [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)
- [DiffSpectra: Molecular Structure Elucidation from Spectra using Diffusion Models](https://mlsanity.com/paper/2507.06853/) (2025)
- [Small molecule retrieval from tandem mass spectrometry: what are we optimizing for?](https://mlsanity.com/paper/2602.16507/) (2026)
- [SpectraLLM: Uncovering the Ability of LLMs for Molecular Structure Elucidation from Multi-Spectral Data](https://mlsanity.com/paper/2508.08441/) (2025)
- [AI Benchmark Democratization and Carpentry](https://mlsanity.com/paper/2512.11588/) (2025)
- [Comparative Analysis of Formula and Structure Prediction from Tandem Mass Spectra](https://mlsanity.com/paper/2601.00941/) (2026)
- [De Novo Molecular Generation from Mass Spectra via Many-Body Enhanced Diffusion](https://mlsanity.com/paper/2602.01643/) (2026)
- [MassSpecGym in the Wild: Uncovering and Correcting Evaluation Pitfalls in AI-Driven Molecule Discovery](https://mlsanity.com/paper/2606.19624/) (2026)
- [Neural Graph Matching Improves Retrieval Augmented Generation in Molecular Machine Learning](https://mlsanity.com/paper/2502.17874/) (2025)
- [SpectrumWorld: Artificial Intelligence Foundation for Spectroscopy](https://mlsanity.com/paper/2508.01188/) (2025)
- [One Small Step with Fingerprints, One Giant Leap for De Novo Molecule Generation from Mass Spectra](https://mlsanity.com/paper/2508.04180/) (2025)
- [An MLCommons Scientific Benchmarks Ontology](https://mlsanity.com/paper/2511.05614/) (2025)
- [Conformal Graph Prediction with Z-Gromov-Wasserstein Distances](https://mlsanity.com/paper/2603.02460/) (2026)
- [FlowMS: Flow Matching for De Novo Structure Elucidation from Mass Spectra](https://mlsanity.com/paper/2603.18397/) (2026)
- [NMIRacle: Multi-modal Generative Molecular Elucidation from IR and NMR Spectra](https://mlsanity.com/paper/2512.19733/) (2025)
- [SpecBridge: Bridging Mass Spectrometry and Molecular Representations via Cross-Modal Alignment](https://mlsanity.com/paper/2601.17204/) (2026)
- [De novo molecular structure elucidation from mass spectra via flow matching](https://mlsanity.com/paper/2602.19912/) (2026)
- [SpecX: A Large-Scale Benchmark for Multi-Modal Spectroscopy and Cross-Paradigm Evaluation](https://mlsanity.com/paper/2605.18791/) (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)
- [How well can off-the-shelf LLMs elucidate molecular structures from mass spectra using chain-of-thought reasoning?](https://mlsanity.com/paper/2601.06289/) (2026)
- [Contrastive Domain Generalization for Cross-Instrument Molecular Identification in Mass Spectrometry](https://mlsanity.com/paper/2602.00547/) (2026)
- [FlexMS: A Unified Public Benchmark for Molecule Tandem Mass Spectrum Prediction](https://mlsanity.com/paper/2602.22822/) (2026)
- [When should we trust the annotation? Selective prediction for molecular structure retrieval from mass spectra](https://mlsanity.com/paper/2603.10950/) (2026)
- [Unlocking High-Fidelity Molecular Generation from Mass Spectra via Dual-Stream Line Graph Diffusion](https://mlsanity.com/paper/2605.07048/) (2026)
- [CoRe-Gen: Robust Spectrum-to-Structure Generation under Imperfect Fingerprint Conditions](https://mlsanity.com/paper/2605.12980/) (2026)

## Abstract (arXiv)

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym -- the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://github.com/pluskal-lab/MassSpecGym.