Finally and GNN (HCat-GNet) approach has also been evaluated. To do this, we took the architecture proposed by Aguilar et al., while the graph representation has been slightly changed. The reason for this is that our dataset is significantly smaller than that used in the original study (about 85% smaller), which makes the learning of the Graph Neural Network significantly more challenging and more likely to overfit on the training samples. To avoid this, we have used the most minimal graph representation that allows correlation between the reaction representation and the selectivity outcome. This way, our graph representation consists of the concatenation of the graph representation of the three participant molecules (substrate, ligand, and solvent). The graph representation of the molecules consisted of a graph where the nodes represent the atoms, while edges represent covalent bonds. We further used node features of atom identity and chirality of the atom, while no edge features were used. By using this simple representation and the original architecture, HCat-GNet attained an RMSE of 20.046% and 0.941 accuracy.