showing chemical reactions and MS/MS spectrum of the reaction with MOP) were selected based on a criteria that either the functional groups were not present in the compounds of the training set or all bootstrapped decision tree models resulted in the prediction to form a diagnostic addition product with MOP. The probabilities of each analyte to form a diagnostic product as assigned by the radius 1 decision tree models are given in Table and for other radii in Table . These tables show that the 60% and 70% cutoffs produced the models best suited for the external test set with their kappa value (0.72) that is greater than for the other cutoff values. The prediction probabilities for the analytes which undergo no diagnostic reaction (#3, #6, #7, and #9) were zero in the 70% cutoff model but above 30% in the 70% cutoff model. Therefore, the 70% cutoff was superior to 60% as it produced lower probabilities of diagnostic addition product formation for analytes that predominantly reacted via proton transfer or not at all. Additionally, other machine learning methods, including regularized logistic regression, k-Nearest Neighbor, and partial least squares classification (Tables ), were evaluated. None of these methods outperformed the 70% decision tree model trained with a fingerprint radius of 1. Finally, the proton affinity model achieved a kappa value of 0.44, indicating that the decision tree model outperformed the manual approach of identifying reactions significantly. One should note that the proton affinity of test reactions #1 and #9, and the calculated proton affinity of MOP are all within 0.1 kcal/mol of each other. Therefore, the correct ordering of these value by the calculated method may happen by chance. Moreover, using the experimental value for the MOP proton affinity instead of the calculated value results in a kappa value of 0.31, further showing the superiority of the decision tree model (kappa = 0.72) over that of the proton affinity calculations.