We proposed a novel feature generation procedure for the representation of DAAA reactions and the training of ML models. This representation is based on simple molecular mechanics calculations of optimized ligand structures, where simple descriptors of steric and electronic factors are used. We combined this reaction representation with different ML algorithms to identify the combination that generated the most accurate predictions. To benchmark our proposal, other two competitor ML methods were compared: RDKit descriptors and the GNN HCat-GNet. We found that the RDKit descriptors perform almost as well as our novel descriptors, while we found that HCat-GNet, although giving satisfactory results, it performed the poorest of the three methods. SHAP analysis was performed to understand the impact of the variables to the models' final predictions. We found that the RDKit descriptors generated a distinct separation in variables that clearly show the direction of impact of each variable. However, these descriptors were less understandable and therefore less useful for de novo design. For our novel features, a clear separation of variables showed the direction of impact to the final prediction, but maintaining high interpretability, which can be used by chemists to ultimately create novel ligands to obtain more efficient transformations. We analyzed the interpretability of HCat-GNet by applying SHAP analysis in terms of its node feature vectors. We found that the models were effectively learning the relation between the configuration of the ligands and the selectivity outcome they induced, which demonstrated the ability of this method to understand to a certain level the relation between reactants and selectivity outcome. Our proposed methodology has shown to be effective to model the reaction, while overcoming limitations of former methods including interpretability and data requirements.