Efficiently predicting high resolution mass spectra with graph neural networks
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 molecular formulas. We 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 orders-of-magnitude faster runtime than state-of-the-art methods.
Cite
@article{arxiv.2301.11419,
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},
journal= {arXiv preprint arXiv:2301.11419},
year = {2023}
}