Predicting a Molecular Fingerprint from an Electron Ionization Mass Spectrum with Deep Neural Networks

Electron
ionization–mass spectrometry (EI-MS) hyphenated
to gas chromatography (GC) is the workhorse for analyzing volatile
compounds in complex samples. The spectral matching method can only
identify compounds within the spectral database. In response, we present
a deep-learning-based approach (DeepEI) for structure elucidation
of an unknown compound with its EI-MS spectrum. DeepEI employs deep
neural networks to predict molecular fingerprints from an EI-MS spectrum
and searches the molecular structure database with the predicted fingerprints.
We evaluated DeepEI with MassBank spectra, and the results indicate
DeepEI is an effective identification method. In addition, DeepEI
can work cooperatively with database spectral matching and NEIMS (fingerprint
to spectrum method) to improve identification accuracy.