Publication details
SpecTUS: SpectralTranslator for Unknown StructuresAnnotation from EI-MS Spectra
| Authors | |
|---|---|
| Year of publication | 2026 |
| Type | Peer-reviewed scientific article |
| Magazine / Source | Analytical chemistry |
| MU Faculty or unit | |
| Citation | |
| web | https://pubs.acs.org/ancham/article/98/29/21670/5207648/SpecTUS-Spectral-Translator-for-Unknown-Structures |
| Doi | https://doi.org/10.1021/acs.analchem.6c02423 |
| Keywords | MASS-SPECTRA; ALGORITHM |
| Attached files | |
| Description | Compound identification and structure annotation from mass spectra are essential in drug detection, forensics, and small molecule discovery. Current approaches to compound identification from electron ionization mass spectra (EI-MS) are dependent on different forms of searching databases that are orders of magnitude smaller than the space of potential molecular structures they attempt to cover. We introduce SpecTUS: Spectral Translator for Unknown Structures, a deep learning model for de novo structural annotation, translating gas chromatography EI-MS spectra directly into molecular structures without requiring reference databases. This enables the identification of novel compounds absent from spectral libraries. In a rigorous evaluation, SpecTUS significantly outperformed standard database search techniques. On a held-out test set of 28,267 spectra from NIST 20, the model's single suggestion perfectly reconstructed 43% of the subset's compounds. On 76% of this test set, the single suggestion is strictly better, in terms of Tanimoto similarity of Morgan fingerprint, than the result of hybrid database search. With ten suggestions, SpecTUS achieved 65% perfect reconstructions, surpassing hybrid search on 84% of the test set. |
| Related projects: |