Top-1 Top- Given the trained GNNs and Transformer models, one can identify important atoms in the molecule. As a note to the reader, the attention maps for the Transformer can be extracted for a specific prediction, even if the reaction presented in the case study is not the top-1 prediction by the Transformer model. In other words, the attention map will always pertain to the case study reaction. The GNN models on the other hand may predict different reaction centres than presented in the case studies. To evaluate a different prediction, two methods are applied: First, if a GNN model predicts the wrong reaction centre (i.e. bond), it is analysed whether the proposed reaction centre by the model is plausible and whether the highlighted atoms are important functional groups to the reaction. Second, the GraphExplainer can be enforced to provide an importance mask for a pre-selected bond/reaction centre. By providing this extra information to the Explainer algorithm, the mask for the case-study reaction can be obtained (and is reported in the ESI Section S4.3).