The diagnostic utility of employing neutral reagents, such as MOP, to identify functional groups in protonated metabolites of a drug. After the metabolites were (a) protonated and isolated, (b) they were allowed to react with MOP and (c) the formation of a diagnostic addition product (DP) as opposed to proton transfer (PT) no reaction were monitored. Only the sulfoxide product generated the diagnostic product ion with MOP. Interpretation of the above experiments is challenging and time-consuming due to large amount of experimental data. In order to facilitate this process, we decided to develop a chemical graph based interpretable machine learning methodology to predict whether a given protonated analyte will form a diagnostic product ion upon reactions with MOP. This task requires the ability to predict the reactivity of a given protonated analyte with the neutral reagent. Multilayer-perceptron, Long-Short Term Memory (LSTM) and Graph Convolution Networks (GCN) approaches have been demonstrated to be suitable for predicting reaction outcomes when a large number of known reactions are available. Unfortunately, due to the specificity of the diagnostic ion-molecule reactions of interest here, only a relatively small set of known reactions exist. Although one-shot and few-shot learning has proven useful in the literature for systems with a small number of observations, these models are typically difficult to interpret and only limited information can be obtained about the reactions. Therefore, a machine learning methodology that can be interpreted by humans is developed in this work.