To confirm our new proposed methodology is effective compared to classical RDKit featurization processes, we have evaluated the performance of such ML models trained to predict on the same variable but using descriptors calculated from the SMILES string of substrate, ligand, and solvent using RDKit. In the case of the ligands, since the whole dataset only has 3 different ligands, we have decided to calculate descriptors related to lipophilicity, polarity, topology, electronics, and electro-topology, which are calculated from the chemical structure (operations on the molecular graph). In the case of the substrates, we have decided to use fragment-count based descriptors since the chemical diversity of these molecules will allow the ML algorithms to learn the contribution of the fragments in terms of electronics and topology separately, rather than calculating a descriptor based on the whole molecule, that might or not capture the effect of specific functional groups. Lastly, for the solvent the Hall-Kier Alpha descriptor was calculated. A hyperparameter optimization process for Random Forest and Gradient Boosting was performed and then a Feature Selection Process was applied. The results obtained are shown in Table . In the case of the results obtained using the RDKit descriptors, GBR is the best performing algorithm for both the regression and classification tasks. The RFR and LR, although perform worse than the GBR, both attain satisfactory results. This means that the RDKit descriptors used do correlate to the selectivity outcome of the reaction to the ligand.