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

31 December 2019, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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 Prospective diagnostic product predictions were made and validated for 14 previously unpublished analytes .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.


Machine Learning
Mass Spectrometry
Reactivity Prediction
Chemical Reactivity Flowchart
Chemical Characterization

Supplementary materials

Fine Liu Supporting 12.26.2019

Supplementary weblinks


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