Alternatively, Figure shows the SHAP analysis of the RFR model using the handcrafted features with the V2 electronic descriptor. Our proposed new features also show a clear trend in how they impact the models' final prediction. For the case of the UL volume of the ligand, high values in general increase the model's prediction (larger %topA or prediction of addition on the top side), while UR volume and LL volume decrease the model prediction (lower %topA or prediction of addition on the bottom side) when the values are bigger. This result agrees with the proposed selectivity model in Figure , which means that the model is effectively learning the relation between the steric of the ligand and the selectivity outcome. Unlike the RDKit descriptors, these ligand descriptors are highly interpretable and ultimately can help chemists to design de novo ligands to optimize this type of conversions. Other properties are found to impact the stereochemical outcome of the reaction, however, these properties depend on the substrate, which is not a molecule to be optimized.