Efficiently predicting high resolution mass spectra with graph neural networks
Michael Murphy, Stefanie Jegelka, Ernest Fraenkel, Tobias Kind, David Healey, Thomas Butler
Abstract
Identifying a small molecule from its mass spectrum is the primary open problem in computational metabolomics. This is typically cast as information retrieval: an unknown spectrum is matched against spectra predicted computationally from a large database of chemical structures. However, current approaches to spectrum prediction model the output space in ways that force a tradeoff between capturing high resolution mass information and tractable learning. We resolve this tradeoff by casting spectrum prediction as a mapping from an input molecular graph to a probability distribution over chemical formulas. We further discover that a large corpus of mass spectra can be closely approximated using a fixed vocabulary constituting only 2% of all observed formulas. This enables efficient spectrum prediction using an architecture similar to graph classification - GrAFF-MS - achieving significantly lower prediction error and greater retrieval accuracy than previous approaches.
BibTeX
@inproceedings{icml2023_efficientlypredi,
title = {Efficiently predicting high resolution mass spectra with graph neural networks},
author = {Michael Murphy and Stefanie Jegelka and Ernest Fraenkel and Tobias Kind and David Healey and Thomas Butler},
booktitle = {ICML 2023},
year = {2023}
}