scholarly journals Graph-based machine learning interprets and predicts diagnostic isomer-selective ion–molecule reactions in tandem mass spectrometry

2020 ◽  
Vol 11 (43) ◽  
pp. 11849-11858
Author(s):  
Jonathan Fine ◽  
Judy Kuan-Yu Liu ◽  
Armen Beck ◽  
Kawthar Z. Alzarieni ◽  
Xin Ma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Tandem Mass Spectrometry ◽  
Chemical Reactivity ◽  
Tandem Mass ◽  
Ion Molecule Reactions ◽  
Diagnostic Ion

We combine mass spectrometry with machine learning that is predictive and explainable using chemical reactivity flowcharts for diagnostic ion–molecule reactions.

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

2019 ◽  
Author(s):  
Jonathan A Fine ◽  
Judy Kuan-Yu Liu ◽  
Armen Beck ◽  
Kawthar Alzarieni ◽  
Xin Ma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Tandem Mass Spectrometry ◽  
Functional Groups ◽  
Chemical Reactivity ◽  
Chemical Characterization ◽  
Training Data ◽  
Tandem Mass ◽  
Tree Model ◽  
Ion Molecule Reactions

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 Prospective diagnostic product predictions were made and validated for 14 previously unpublished analytes .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

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

2019 ◽  
Author(s):  
Jonathan A Fine ◽  
Judy Kuan-Yu Liu ◽  
Armen Beck ◽  
Kawthar Alzarieni ◽  
Xin Ma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Tandem Mass Spectrometry ◽  
Functional Groups ◽  
Chemical Reactivity ◽  
Chemical Characterization ◽  
Training Data ◽  
Tandem Mass ◽  
Tree Model ◽  
Ion Molecule Reactions

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.<br>

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