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Rapid Prediction of Electron-Ionization Mass Spectrometry Using Neural Networks

Author(s): Wei, JN; Belanger, D; Adams, Ryan P; Sculley, D

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Abstract: When confronted with a substance of unknown identity, researchers often perform mass spectrometry on the sample and compare the observed spectrum to a library of previously collected spectra to identify the molecule. While popular, this approach will fail to identify molecules that are not in the existing library. In response, we propose to improve the library's coverage by augmenting it with synthetic spectra that are predicted from candidate molecules using machine learning. We contribute a lightweight neural network model that quickly predicts mass spectra for small molecules, averaging 5 ms per molecule with a recall-at-10 accuracy of 91.8%. Achieving high-accuracy predictions requires a novel neural network architecture that is designed to capture typical fragmentation patterns from electron ionization. We analyze the effects of our modeling innovations on library matching performance and compare our models to prior machine-learning-based work on spectrum prediction.
Publication Date: 2019
Citation: Wei, JN, Belanger, D, Adams, RP, Sculley, D. (2019). Rapid Prediction of Electron-Ionization Mass Spectrometry Using Neural Networks. ACS Central Science, 5 (700 - 708. doi:10.1021/acscentsci.9b00085
DOI: doi:10.1021/acscentsci.9b00085
Pages: 700 - 708
Type of Material: Journal Article
Journal/Proceeding Title: ACS Central Science
Version: Final published version. This is an open access article.

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