Cutoff Assignments for Machine Learning Model. Since the experimental outcome of a given analysis was either proton transfer/no reaction, or the formation of a diagnostic addition product ion (see Figure ), and a limited amount of data is available for training, a binary classifier is preferable to other supervised machine learning models. The training set for this classifier includes a set of 36 protonated analytes where the reactions with MOP have been studied along with their product branching ratios for the addition reaction (see Table for all MOP reactions). The distribution of product branching ratios measured for the diagnostic addition reaction (see Figure ) show a large gap between 65% -83% as no compounds had a diagnostic product branching ratio between this percentage gap. This gap indicated that cutoff of 70% or greater for the branching ratio should be used in this binary classifier to determine whether a given analyte will undergo the diagnostic addition reaction with MOP.