Roman Bushuiev; Anton Bushuiev; Niek F. de Jonge; Adamo Young; Fleming Kretschmer; Raman Samusevich; Janne Heirman; Fei Wang; Luke Zhang; Kai Dührkop; Marcus Ludwig; Nils A. Haupt; Apurva Kalia; Corinna Brungs; Robin Schmid; Russell Greiner; Bo Wang; David S. Wishart; Li-Ping Liu; Juho Rousu; Wout Bittremieux; Hannes Rost; Tytus D. Mak; Soha Hassoun; Florian Huber; Justin J.J. van der Hooft; Michael A. Stravs; Sebastian Böcker; Josef Sivic; Tomáš Pluskal

Abstract

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.