MEGAN and RetroXpert, for instance, produce 2 to 4 invalid SMILES within the top-20 precursor sets. No metric could however be computed for G 2 Retro as the invalid predictions are filtered within the model. Furthermore, the "invalidity" for template-based models is reported. As template-based models guarantee to return a valid chemical transformation, the invalidity herein refers to the inability to retrieve a relevant template that matches the target. This happens after a particular beam size k. This fault however is attributed to the restricted pool of templates that the model can choose from. The influence on the top-10/20 invalidity is negligible and thus can be disregarded. The models can be compared to literature precedent: For the EGAT, this study outperforms literature values by 5% for the top-1 accuracy. It is assumed that the model was thus reproduced correctly. However, a deterioration in accuracy is seen for the D-MPNN. Particularly, Somnath et al. reports higher values for the top-1/2 accuracies. The reason is two-fold: First, the number of message passing layers is kept to T = 5 instead of T = 10 as done in GraphRetro. This is because increasing the number of layers (increasingly) convolutes the input features of the nodes and edges. Finding a meaningful mask with the GNNExplainer becomes harder and was found to be difficult for T = 10. Second, the model in this paper is simplified as it does not utilise the bond score update network as in GraphRetro.