To better understand what the QM properties do to the model, we train two additional models. The first model we name GCN (Atom type) and is the most basic model we trained. The GCN (Atom type) model only contains node features that describe the kind of atom (same atom encoding as the GCN (RDKit) model). The second model is called GCN (Atom type + QM) extends the GCN (Atom type) model by including the six QM atom properties. Unsurprisingly, the GCN (Atom type) model performs the worst since it contains much less information about the atoms. The top-1 mean accuracy (Table ) drops from 58.6% to 48.8% compared to the GCN (RDKit) model. However, when the QM properties are included, the top-1 accuracy increases to 56.5%, which is only a decrease of 2.1% points compared to the GCN (RDKit) model and only a drop of 0.7% points for the top-50 accuracy. This suggests that there is a significant correlation between the RDKit features and the QM property features we chose. Such correlations can explain why we see no differences in accuracy between the GCN (RDKit) and GCN (RDKit + QM) model since they, in essence, carry similar information about each atom. It also explains why the GCN (Atom type + QM) and the GCN (RDKit) model almost perform the same.