2. For simpler case studies, the graph models are observed to provide adequate reasoning for their prediction. Even if the identified reaction centre is incorrect, the highlighted functional groups provide reasoning for the model's prediction (e.g. Test molecule 4 -EGAT, Salmeterol -D-MPNN). Nonetheless, as the molecule becomes more complex with multiple potential reaction centres (Salmeterol) or more difficult with respect to the mechanism (Waferin), the graph models are more prone to provide unreasonable disconnection sites. Since the input features to the GNNs are often chosen randomly, the model is naturally disadvantaged in understanding the underlying chemistry. The GNN model would therefore benefit from better featurisation (detailed in Section 4). As a note to the reader, the GNNExplainer is a useful tool, but not robust. It requires both model and hyperparameter fine-tuning. The fidelity metric remedies some of its flaws, informing the user of uncertainty in the subgraph selection. However, with better chemical descriptors as input features, the model interpretability could possibly be inferred directly from the descriptor (or through dimensionality reduction techniques).