ArticleAnalytical chemistry2024
Quantum Chemistry-Based Prediction of Electron Ionization Mass Spectra for Environmental Chemicals.
Article in Analytical chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- SpecTUS: Spectral Translator for Unknown Structures Annotation from EI-MS Spectra.Analytical chemistry · 2026Article
- Forging Sustainable Carbon-Nitrogen Bonds from COChemSusChem · 2026Review
- Refining EI-MS library search results through atomic-level insights.Communications chemistry · 2025Article
- Galaxy QCxMS for straightforward semi-empirical quantum mechanical EI-MS prediction.GigaByte (Hong Kong, China) · 2025Article
- Quantum Chemistry-Based Prediction of Electron Ionization Mass Spectra for Environmental Chemicals.Analytical chemistry · 2024Article
- High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage.Environmental science & technology · 2024Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There is a lack of experimental electron ionization high-resolution mass spectra available to assist compound identification. The in silico generation of mass spectra by quantum chemistry can aid annotation workflows, in particular to support the identification of compounds that lack experimental reference spectra, such as environmental chemicals. We present an open-source, semiautomated workflow for the in silico prediction of electron ionization high-resolution mass spectra at 70 eV based on the QCxMS software. The workflow was applied to predict the spectra of 367 environmental chemicals, and the accuracy was evaluated by comparison to experimental reference spectra acquired. The molecular flexibility, number of rotatable bonds, and number of electronegative atoms of a compound were negatively correlated with prediction accuracy. Few analytes are predicted to sufficient accuracy for the direct application of predicted spectra in spectral matching workflows (overall average score 428). The
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Registered trials
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