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Graph Based Machine Learning Interprets Diagnostic Isomer-Selective Ion-Molecule Reactions in Tandem Mass Spectrometry

preprint
submitted on 28.12.2019 and posted on 31.12.2019 by Jonathan A Fine, Judy Kuan-Yu Liu, Armen Beck, Kawthar Alzarieni, Xin Ma, Victoria Boulos, Hilkka Kenttämaa, Gaurav Chopra
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 Prospective diagnostic product predictions were made and validated for 14 previously unpublished analytes .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.

Funding

Integrated Data Science Institute Award

Department of Chemistry Start-up Funds at Purdue University

Purdue University Center for Cancer Research, NIH grant P30 CA023168

History

Email Address of Submitting Author

gchopra@purdue.edu

Institution

Purdue University

Country

United States

ORCID For Submitting Author

0000-0003-0942-7898

Declaration of Conflict of Interest

None

Version Notes

version-1.0

Licence

Exports