Journal article
Jonathan A Fine, Judy Kuan-Yu Liu, Armen Beck, Kawthar Z. Alzarieni, Xin Ma, Victoria M Boulos, H. Kenttämaa, G. Chopra
Chemical Science, 2020
Chemical Science, 2020
Semantic Scholar
DOI
PubMedCentral
PubMed
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APA
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Fine, J. A., Liu, J. K.-Y., Beck, A., Alzarieni, K. Z., Ma, X., Boulos, V. M., … Chopra, G. (2020). Graph-based machine learning interprets and predicts diagnostic isomer-selective ion–molecule reactions in tandem mass spectrometry†. Chemical Science.
Chicago/Turabian
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Fine, Jonathan A, Judy Kuan-Yu Liu, Armen Beck, Kawthar Z. Alzarieni, Xin Ma, Victoria M Boulos, H. Kenttämaa, and G. Chopra. “Graph-Based Machine Learning Interprets and Predicts Diagnostic Isomer-Selective Ion–Molecule Reactions in Tandem Mass Spectrometry†.” Chemical Science (2020).
MLA
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Fine, Jonathan A., et al. “Graph-Based Machine Learning Interprets and Predicts Diagnostic Isomer-Selective Ion–Molecule Reactions in Tandem Mass Spectrometry†.” Chemical Science, 2020.
BibTeX Click to copy
@article{jonathan2020a,
title = {Graph-based machine learning interprets and predicts diagnostic isomer-selective ion–molecule reactions in tandem mass spectrometry†},
year = {2020},
journal = {Chemical Science},
author = {Fine, Jonathan A and Liu, Judy Kuan-Yu 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 employed in tandem mass spectrometry experiments can frequently be used to differentiate between isomeric compounds unlike the popular collision-activated dissociation methodology. Selected neutral reagents, such as 2-methoxypropene (MOP), are introduced into an ion trap mass spectrometer where they react with protonated analytes to yield product ions that are diagnostic for the functional groups present in the analytes. However, the understanding and interpretation of the mass spectra obtained can be challenging and time-consuming. Here, we introduce the first bootstrapped decision tree model trained on 36 known ion–molecule reactions with MOP. It uses the graph-based connectivity of analytes' functional groups as input to predict whether the protonated analyte will undergo a diagnostic reaction with MOP. A Cohen kappa statistic of 0.70 was achieved with a blind test set, suggesting substantial inter-model reliability on limited training data. Prospective diagnostic product predictions were experimentally tested for 13 previously unpublished analytes. We introduce chemical reactivity flowcharts to facilitate chemical interpretation of the decisions made by the machine learning method that will be useful to understand and interpret the mass spectra for chemical reactivity.
