To ensure that result is not just a consequence of the uneven distribution of RDKit and QM atom features. The input node embedding in the GCN (RDKit + QM) contains 32 values in total, and only six of those are QM properties. We train two extra models where the RDKit part of the GCN (RDKit) and GCN (RDKit + QM) models are compressed into a six and 12-dimensional vector, respectively. By compressing the RDKit part of the GCN (RDKit + QM) before concatenating with the six QM properties, we transform the node embedding into a 12-dimensional feature vector with equal weight on the RDKit and QM features. The compression is performed by passing the RDKit input feature vector through a dense linear layer with a ReLU activation function (red layer in Figure ) before concatenating with the QM descriptors. Again, we observe no significant differences between the two models when comparing the mean accuracies in Table or the cumulative accuracy in Figure . If anything, the GCN (12 RDKit) model performs slightly better than the GCM (6 RDKit, 6 QM). These findings refute our hypothesis that the lack of differences is a result of uneven distribution of RDKit and QM features.