To establish a baseline for the graph neural network type of model, we train a graph neural network that essentially follows an implementation similar to DeepChem's GCN networks . The node features are extracted directly from the RDKit molecular object graph of the product. They include information about the atom type, number of directly bonded neighbours, formal charge, hybridization, automaticity, ring information, and atomic mass. Which is very similar to the atomic information used to construct the ECFP representation . We call this model GCN (RDKit) as it only contains atom features extracted directly from RDKit. As illustrated in Table , the mean accuracy of the baseline GCN (RDKit) model is quite similar to the public ECFP recommender model. If anything, the top-5, top-10, and top-50 accuracy of the GCN (RDKit) model slightly outperform the ECFP recommender model with approximately 1% point. However, as stated in the introduction, ML models generally perform worse for less frequent data. Due to the very imbalanced nature of the data, the mean accuracy across the entire test hides details in the performance. As Illustrated in Figure , which shows the mean accuracy for the cumulative template occurrence, there is a distinct drop in accuracy for less frequent reactions. The top-1 accuracy of the templates that occur less than or equal five times is roughly half as accurate as the templates that occur more than 100 times: 34.8% compared to 65.7% for the ECFP model, and 33.5% compared to 65.0% for the and GCN (RDKit). Figure illustrates that the insignificant differences in the mean accuracy hid some noteworthy differences between the two models. It is apparent that the GCN (RDKit) model generally performs better for the rare reactions, and this trend is most evident for the top-50 accuracy where we find a difference of 8.5% between the two models for templates that occur less than or equal to five times. This slight rise in accuracy for the GCN (RDKit) model can likely be attributed to the convolutional layers, which in essence, build up a custom fingerprint that is trained towards recommending which template to use. Next, we investigate what happens if QM properties are included in the node feature vector. Therefore, we extend the GCN (RDKit) atom feature input with six additional GFN2-xTB QM features: the partial charge (q), the Fukui index (f+, f0, f-), the sum of Wiberg covalent bond orders (sBO), and the polarizability (α). We could have calculated many different molecular and atomic properties, which could have been used as descriptors. However, many of the properties may carry redundant information, and testing all combinations of available descriptors at different levels of theory is beyond the scope of this study. When including QM properties, we remove the corresponding RDKit properties, i.e. the formal charge and number of directly bonded neighbours. The extended model, we give the name GCN (RDKit + QM). The mean accuracies for the GCN (RDKit) and GCN (RDKit + QM) model (listed in Table ) are practically identical with only insignificant changes of maximally 0.1% point. The same trend is observed in the cumulative accuracy in Figure where it is hard to distinguish the two models.