Creation and evaluation of the Decision Tree models. The prediction of adduct formation of a protonated analyte with MOP is possible through a combination of fingerprinting techniques and corresponding machine learning techniques. For each reaction, the analyte and adduct were written as a stoichiometrically-balanced reaction-SMILES string. The field for the 'name' of the reaction is annotated with the diagnostic product ratio as shown in Table . This reaction was then converted to a Morgan fingerprint 40 using the RDkit software package with a radius of one, two, and three atoms and a bit length of 2048 bits. For the sample case presented herein, 36 reactions (training set) of known protonated analytes with the MOP reagent were examined in the decision tree model and each reaction was assigned a binary response of "no-hit" or "hit" based on the branching ratios of the product. The decision tree models were created using, the Julia implementation of Decision Tree, DecisionTree.jl () using a minimum leaf size of 2 to reduce overfitting to a single analyte. The decision tree classification model increases the information gain (IG) of a given classification (c, in this case simply "no-hit" or "hit") by decreasing the entropy (H) of a given set (S) which is calculated using equation (2) using the probability of finding a specific class (𝑝(𝑐)). Information gain is the difference in entropy for the full set (S) and the sum of the entropies for all subsets (t) created by all splits (A) as shown in equation (3). 𝐻(𝑆) = ( -𝑝(𝑐) log -𝑝(𝑐) .∊0