They trained a GCN model using around 45,000 reaction pairs extracted from QM9/G4(MP2) dataset. Their model performed very well on five-fold cross-validation, however, showed poor performance on the external test set from the NIST database, especially on structures that were underrepresented in the training data. It is not possible to compare their results with ours directly since the models are trained using different datasets covering different chemical spaces. QM9/G4(MP2) dataset contains compounds up to nine atoms and comprise five elements (C, H, O, N, and F) while RedDB contains AORFB-focused compounds up to 46 atoms and comprise six different elements (C, H, O, N, F, and S) at max. Nevertheless, both studies show the possibility of data-driven discovery of candidate AORFB compounds by their accurate predictions on a defined chemical space. They also underline the danger of the inapplicability of data-driven models on the outside of this chemical space.