International Journal of Quantum Chemistry
International Journal of Quantum Chemistry
3 years ago

Quantum chemical electron impact mass spectrum prediction for de novo structure elucidation: Assessment against experimental reference data and comparison to competitive fragmentation modeling

Quantum chemical electron impact mass spectrum prediction for de novo structure elucidation: Assessment against experimental reference data and comparison to competitive fragmentation modeling
Amir Karton, Dylan Jayatilaka, Björn Bohman, Peter R. Spackman
We investigate the success of the quantum chemical electron impact mass spectrum (QCEIMS) method in predicting the electron impact mass spectra of a diverse test set of 61 small molecules selected to be representative of common fragmentations and reactions in electron impact mass spectra. Comparison with experimental spectra is performed using the standard matching algorithms, and the relative ranking position of the actual molecule matching the spectra within the NIST-11 library is examined. We find that the correct spectrum is ranked in the top two matches from structural isomers in more than 50% of the cases. QCEIMS, thus, reproduces the distribution of peaks sufficiently well to identify the compounds, with the RMSD and mean absolute difference between appropriately normalized predicted and experimental spectra being at most 9% and 3% respectively, even though the most intense peaks are often qualitatively poorly reproduced. We also compare the QCEIMS method to competitive fragmentation modeling for electron ionization, a training-based mass spectrum prediction method, and remarkably we find the QCEIMS performs equivalently or better. We conclude that QCEIMS will be very useful for those who wish to identify new compounds which are not well represented in the mass spectral databases. The accurate prediction of electron impact mass spectra would greatly expedite the process of unknown compound identification or de-novo structure elucidation. The capabilities of ab-initio methods and a machine learning methods as compared to reference to experimental spectra, demonstrate great potential for future practical applications in de-novo structure elucidation.

Publisher URL: http://onlinelibrary.wiley.com/resolve/doi

DOI: 10.1002/qua.25460

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