Journal article
Jonathan A Fine, J. Liu, Armen Beck, Kawthar Z. Alzarieni, Xin Ma, Victoria M Boulos, H. Kenttämaa, G. Chopra
2019
2019
Semantic Scholar
DOI
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APA
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Fine, J. A., Liu, J., Beck, A., Alzarieni, K. Z., Ma, X., Boulos, V. M., … Chopra, G. (2019). Graph Based Machine Learning Interprets Diagnostic Isomer-Selective Ion-Molecule Reactions in Tandem Mass Spectrometry.
Chicago/Turabian
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Fine, Jonathan A, J. Liu, Armen Beck, Kawthar Z. Alzarieni, Xin Ma, Victoria M Boulos, H. Kenttämaa, and G. Chopra. “Graph Based Machine Learning Interprets Diagnostic Isomer-Selective Ion-Molecule Reactions in Tandem Mass Spectrometry” (2019).
MLA
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Fine, Jonathan A., et al. Graph Based Machine Learning Interprets Diagnostic Isomer-Selective Ion-Molecule Reactions in Tandem Mass Spectrometry. 2019.
BibTeX Click to copy
@article{jonathan2019a,
title = {Graph Based Machine Learning Interprets Diagnostic Isomer-Selective Ion-Molecule Reactions in Tandem Mass Spectrometry},
year = {2019},
author = {Fine, Jonathan A and Liu, J. and Beck, Armen and Alzarieni, Kawthar Z. and Ma, Xin and Boulos, Victoria M and Kenttämaa, H. and Chopra, G.}
}
Abstract
Diagnostic ion-molecule reactions using tandem mass spectrometry can differentiate between isomeric compounds unlike a popular collision-activated dissociation methodology for the identification of previously unknown mixtures. Selected neutral reagents, such as 2-methoxypropene (MOP) are introduced into an ion trap mass spectrometer and react with protonated analytes to produce product ions diagnostic of the functional groups present in the analyte. However, the interpretation and understanding of specific reactions are challenging and time-consuming for chemical characterization. Here, we introduce a first bootstrapped decision tree model trained on 36 known ion-molecule reactions with MOP using graph-based connectivity of analyte’s functional groups as input. A Cohen Kappa statistic of 0.72 was achieved, suggesting substantial inter-model reliability on limited training data. Prospective diagnostic product predictions were made and validated for 14 previously unpublished analytes . Chemical reactivity flowcharts were introduced to understand the decisions made by the machine learning method that will be useful for chemists.
