  The accurate prediction of tandem mass spectra from molecular structures has
the potential to unlock new metabolomic discoveries by augmenting the
community's libraries of experimental reference standards. Cheminformatic
spectrum prediction strategies use a "bond-breaking" framework to iteratively
simulate mass spectrum fragmentations, but these methods are (a) slow, due to
the need to exhaustively and combinatorially break molecules and (b)
inaccurate, as they often rely upon heuristics to predict the intensity of each
resulting fragment; neural network alternatives mitigate computational cost but
are black-box and not inherently more accurate. We introduce a
physically-grounded neural approach that learns to predict each breakage event
and score the most relevant subset of molecular fragments quickly and
accurately. We evaluate our model by predicting spectra from both public and
private standard libraries, demonstrating that our hybrid approach offers state
of the art prediction accuracy, improved metabolite identification from a
database of candidates, and higher interpretability when compared to previous
breakage methods and black box neural networks. The grounding of our approach
in physical fragmentation events shows especially high promise for elucidating
natural product molecules with more complex scaffolds.
