Table . The probability of reaction assigned for all decision tree models a Value greater than the proton affinity of MOP (214.42 kcal/mol) as calculated using Density Functional Theory, see section Calculation of proton affinities in methods for details ** See Figures for assignment of diagnostic production formation Given the interpretability of decision tree models, we introduce a chemical reactivity flowchart to interpret the logic behind the 70% model used to make predictions. The decision tree flow chart for the 70% cutoff and fingerprint radius of 1 atom is given in Figure and supplementary diagrams for the 40% cutoff are provided in Figure . The logic begins by checking for a sulfoxide with at least one aliphatic atom bound to it and assigns the analyte as "reactive" if this functional group is present (see Figure ). Then, the model checks for nitrogen with three substitutions in a heteroaromatic ring and assigns "reactivity" if such an atom is present. If neither functional group is present, then the model checks for a junction between sp 2 hybridized atoms and assigns analytes containing this group as "reactive". If this group is not present, the model then checks for a sulfoxide located next to an aromatic ring and assigns the compound as "reactive" if the sulfoxide is between two aromatic rings. Then, the model checks for a terminal carbon off of any atom and assigns all analytes lacking this functionality as those that will not undergo the diagnostic reaction (hence, are "unreactive"). Those analytes that contain this functionality are checked for terminal oxygens or carbonyl groups and compounds lacking these functionalities are checked for hydroxylamino group for final "reactivity" assignment. It should be noted that these features are identified by the trained decision tree model that make chemical sense for several cases, such as, presence of sulfoxide with at least one aliphatic atom bound to it (feature) resulting in the diagnostic product with MOP (Figure ). All cutoff models correctly predicted the three test sulfoxide compounds (#4, #11, #14 in Table ) were "reactive" towards MOP with 100% certainty; a result which could be explained by the fact that all sulfoxides in the training set, except for one, had a reaction efficiency greater than 40%. Therefore, this result reflected the true experimental conclusion regarding sulfoxide compounds. This concept was reflected by the presence of a sulfoxide group as the paramount feature in the model (at the top of Figure ). Similarly, all the models predicted that analytes containing an N-oxide functionality are "reactive" (#8, #10, #12, #13). However, experiment results show that compounds containing nitro groups (#12, #10) were "unreactive" (does not undergo a diagnostic addition reaction). These two compounds represented the only two errors made by the 70% cutoff model and these failures may be due to a nitro group not being present in compounds in the training set. The proton affinity model, however, correctly predicted these two nitro compounds as "unreactive" towards MOP, suggesting that when new functional groups were added to the model, a proton affinity verification step could be used to ensure that the new reaction predictions are correct. Since proton affinity model incorrectly predicted that compound #2, #3, and #6 will form diagnostic addition products and that compound #1 will not, and none of these compounds contain functional groups present in the training set, it is best to apply this verification only if the compound contains functional groups not present in compounds in the original training set.