To elucidate the structural features required to achieve either moderate (30:70 N to 14 N) or high (80:20 N to 14 N) isotopic ratios in the labeled products of this reaction (Figure ), we computed DFT descriptors for both the neutral starting materials and the corresponding triflylated intermediates. These thresholds were selected since 30% N labeling is likely adequate for practical applications, while achieving 80% labeling demonstrates the efficiency of the reaction. We subjected the entire set of pyrimidines and simple pyridines (in Figures and) to single-node decision trees with the exception of the only examples of 2-substituted pyrimidines (i.e., 28 and 35; see computational and modeling details in the Supplementary Materials). We validated the models using the set of complex pyridine substrates depicted in Figure , since these substrates represent medicinally relevant, complex examples. Using the 30% N incorporation threshold (Figure ), the classification algorithm shows that substrates can be effectively binned according to the computed N-C6 distance in the parent compound (prior to triflylation) with 94% accuracy. Substrates with computed N-C6 bond distance >1.330 Å consistently yield N incorporation above 30%. This structural feature also suggests that as the N-C6 distance increases, the nitrogen atom becomes more nucleophilic and thus undergoes triflylation more effectively. Additionally, a longer N-C6 bond could also indicate reduced steric hindrance at nitrogen, further facilitating triflylation. The model misclassified two pyrimidines (32 and 33), both of which possess a para-methoxyphenyl group at C4. As mentioned before, these compounds may form stronger N-S bonds, rendering them more resistant to detriflylation and increasing the observed isotopic yield relative to what is expected from the model. Finally, we validated the model's ability to predict isotopic yields using the complex pyridine set. Notably, none of the simple pyridines in the training set yielded 15 N incorporation below 30%, which led us to anticipate that testing the model's performance against pyridines with varied substitution, with their diverse isotopic yield range, could serve as a robustness test for the model. The validation test resulted in an 83% accuracy, demonstrating the potential applicability of this model.