Mass Spectrometry
Online ISSN : 2186-5116
Print ISSN : 2187-137X
ISSN-L : 2186-5116
Original Article
Construction of a Mass Spectrum Library Containing Predicted Electron Ionization Mass Spectra Prepared Using a Machine Learning Model and the Development of an Efficient Search Method
Ayumi Kubo Azusa KubotaHaruki IshiokaTakuhiro HizumeMasaaki UbukataKenji NagatomoTakaya SatohMitsuyoshi YoshidaFuminori Uematsu
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Supplementary material

2023 Volume 12 Issue 1 Pages A0120

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Abstract

Electron ionization (EI) mass spectrum library searching is usually performed to identify a compound in gas chromatography/mass spectrometry. However, compounds whose EI mass spectra are registered in the library are still limited compared to the popular compound databases. This means that there are compounds that cannot be identified by conventional library searching but also may result in false positives. In this report, we report on the development of a machine learning model, which was trained using chemical formulae and EI mass spectra, that can predict the EI mass spectrum from the chemical structure. It allowed us to create a predicted EI mass spectrum database with predicted EI mass spectra for 100 million compounds in PubChem. We also propose a method for improving library searching time and accuracy that includes an extensive mass spectrum library.

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© 2023 Ayumi Kubo, Azusa Kubota, Haruki Ishioka, Takuhiro Hizume, Masaaki Ubukata, Kenji Nagatomo, Takaya Satoh, Mitsuyoshi Yoshida, and Fuminori Uematsu. This is an open-access article distributed under the terms of Creative Commons Attribution Non-Commercial 4.0 International License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

This article is licensed under a Creative Commons [Attribution-NonCommercial 4.0 International] license.
https://creativecommons.org/licenses/by-nc/4.0/
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