The work presented here demonstrated that a combination of machine learning and tandem mass spectrometry experiments based on diagnostic ion-molecule reactions can be used to identify analytes in a semiautomated fashion while generating results in a manner readily understandable to chemists. This machine learning methodology combined an automated functional group identification method (Morgan Fingerprinting) with a decision tree model trained on only 36 analytes and was prospectively validated using 14 external analytes of unknown experimental outcomes. The model correctly predicted 12 of the 14 analyte reactions with MOP present in the test set without any additional proton affinity-based QM calculations, and 14 of 14 analyte reactions when an additional QM filter was applied. In addition to outperforming other traditional machine learning models, the decision tree model is easily interpretable by humans using newly introduced chemical reactivity flowcharts. Additionally, the inclusion of new data results in simple changes to the model as opposed to the creation of an entirely new model suggesting robust selection of chemical features. The methodologies presented herein will pave the way for expanding the above MS/MS method to include new diagnostic reactions for the identification of, for example, drug metabolites in an easy, accurate, and automated manner. The ultimate goal of this research is to determine isomeric metabolites of medicinal compounds via the identification of diagnostic product ion formed with selected neutral reagents. In the future, a fully automated pipeline for mixture component identification incorporating multiple models similar to the one presented here will be showcased along with how this methodology can be used to aid in the development of new therapeutics. The detailed output of all machine learning models is given in the Supporting Information along with the MS/MS spectra measured for all MOP reactions not previously characterized in the literature. Additionally, all computer code, machine learning inputs, and other relevant scripts are provided on our GitHub page: .