2025/02/07 by Hájek, Adam, Hecht, Helge, Price, Elliott J. +1
#Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Statistics and Probability (physics.data-an)
paper · doi:10.48550/arxiv.2502.05114
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 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 \numprint28267 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.