Comparative Evaluation of Electron Ionization Mass Spectral Prediction Methods

During the past decade promising methods for computational
prediction
of electron ionization mass spectra have been developed. The most
prominent ones are based on quantum chemistry (QCEIMS) and machine
learning (CFM-EI, NEIMS). Here we provide a threefold comparison of
these methods with respect to spectral prediction and compound identification.
We found that there is no unambiguous way to determine the best of
these three methods. Among other factors, we find that the choice
of spectral distance functions play an important role regarding the
performance for compound identification.