MSNovelist: de novo structure generation from mass spectra

Current methods for structure elucidation of small molecules rely on finding similarity with spectra of known compounds but do not predict structures de novo for unknown compound classes. We present MSNovelist, which combines fingerprint prediction with an encoder-decoder neural network to generate structures de novo solely from tandem mass spectrometry (MS²) spectra. In evaluations using 3,863 MS² spectra and the CASMI 2016 challenge, MSNovelist demonstrated a significant ability to predict novel structures. Its application to a bryophyte MS² dataset further highlighted its capabilities in de novo structure prediction, outperforming database searches for several spectra. MSNovelist is a valuable tool to complement library-based annotation, especially for poorly represented analyte classes and novel compounds.

Michael A. Stravs, Kai Dührkop, Sebastian Böcker, Nicola Zamboni