From the superclass distribution presented in Figure , the similarity index SJS can be calculated. As mentioned within the main body, a similar distribution suggests that the algorithm has learned from the data without exhibiting a bias toward specific reaction classes in its predictions. However, it is worth noting that both semi-template and template-based algorithms tend to propose a broader range of reactions, but they are also more prone to bias toward certain reaction classes. Upon closer examination of Figure , it becomes apparent that GraphRetro and G2Retro (semi-template models) struggle to predict heterocycle formations, with GraphRetro showing a preference for oxidation/reduction reactions. Both of these models exhibit a similarity SJS ≤ 0.9. In the case of template-based models, they exhibit a greater bias toward functional group interconversions and are less likely to make predictions related to functional group additions (FGA). This bias may be attributed to the relatively low occurrence of FGA reactions in the dataset, potentially leading template-based frameworks to generally disregard FGA templates. This observation holds particularly true for MHNReact, which again has a similarity SJS ≤ 0.9. A soft constraint on the similarity index of 0.9 is suggested to assess the ability of the framework to propose all types of reactions without exhibiting observable bias. The GNNExplainer is queried specifically for the node importance in the amide-bond formation (Figure . The model identifies the carbonyl correctly. Nonetheless, importance is given to a terminal carbon chain, which is irrelevant to the reaction.