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arXiv · 2502.05114

SpecTUS: Spectral Translator for Unknown Structures annotation from EI-MS spectra

Adam Hájek·Michal Starý·Elliott Price·Filip Jozefov·Helge Hecht·Aleš Křenek
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Abstract

Compound identification and structure annotation from mass spectra is a well-established task widely applied in drug detection, criminal forensics, small molecule biomarker discovery and chemical engineering. We propose SpecTUS: Spectral Translator for Unknown Structures, a deep neural model that addresses the task of structural annotation of small molecules from low-resolution gas chromatography electron ionization mass spectra (GC-EI-MS). Our model analyzes the spectra in \textit{de novo} manner -- a direct translation from the spectra into 2D-structural representation. Our approach is particularly useful for analyzing compounds unavailable in spectral libraries. In a rigorous evaluation of our model on the novel structure annotation task across different libraries, we outperformed standard database search techniques by a wide margin. On a held-out testing set, including \numprint{28267} spectra from the NIST database, we show that our model's single suggestion perfectly reconstructs 43\% of the subset's compounds. This single suggestion is strictly better than the candidate of the database hybrid search (common method among practitioners) in 76\% of cases. In a~still affordable scenario of~10 suggestions, perfect reconstruction is achieved in 65\%, and 84\% are better than the hybrid search.

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Adam Hájek, Michal Starý, Elliott Price, Filip Jozefov, Helge Hecht, Aleš Křenek. 2026-02-20. SpecTUS: Spectral Translator for Unknown Structures annotation from EI-MS spectra. https://arxiv.org/abs/2502.05114

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