The Explainer specialises in graph objects and cannot be utilised for the SMILES-based models. Interpreting the Transformer architecture is inherently more difficult due to the large number of parameters contained within the model. An attempt to interpret the Transformer for retrosynthesis has been made by Kovács et al. for forward synthesis. In their study, input attribution determines functional groups within the reactants that contribute to the predicted product. Three different reaction types were investigated with the conclusion that the Transformer architecture memorises patterns in the database. We aim to either confirm or reject this hypothesis. Particularly, this is achieved by utilising attention maps. Attention maps assign an importance score between each token in the reactant SMILES to each token in the product SMILES. These attention weights can be plotted directly on the molecule after performing a column-wise summation. Each token in the product SMILES is thus assigned its total importance (attention) with respect to the reactant SMILES. The attention weights are obtained from the cross-attention head of the decoder section within the Transformer. The attention weights are calculated through: