Following the workflow previously described for model building, the iterative process afforded the combination of structural parameters resulting in the model depicted in Figure . The catalyst data set was partitioned using the Kennard-stone split method into 80:20 training:validation set. External validation techniques (k-fold = 0.85 and R 2 pred = 0.81) indicated a statistically robust model. In addition, the change of the training/test splitting method did not impact the model equation (Figure ). Among the best models obtained in the iteration process, the presented model was selected based on the statistical scores and predictability. The appearance of related parameters among the series of most successful models indicates that we have identified the global operating components of this class of tetradentate chiral manganese catalysts through our workflow (Figure ). However, since all of them combine structural parameters, the interpretability of a singular model does not affect the overall chemical understanding of the reaction.