When comparing the three approaches, the novel proposed featurization process outperforms the RDKit featurization and HCat-GNet approaches for this sparse data set. In the case of HCat-GNet, the biggest limitation found is its poor performance due to the lack of data, while for the RDKit descriptors, although attain high accuracies, are harder to interpret, which makes de novo ligand design from them more challenging. Our novel features are demonstrated to competently represent the reaction and are highly interpretable. This means that the models are not only useful to predict selectivity of new reactions, but also the conclusions from SHAP analysis can be used to ultimately optimize a ligand structure to obtain more efficient transformations.