A prerequisite to node identification is the necessity of a trained GNN. For this purpose, we train two types of GNNs: i) Edge-aware Graph Attention Network (EGAT) and ii) Direct Message Passing Neural Network . These two architectures were chosen as they are employed by a large selection of semi-template frameworks such as GraphRetro, G 2 Retro, Graph2Edits, RetroXpert and MEGAN. Implementation details for the two GNNs can be found within the SI (Section S3). Both GNNs were trained on the cross-entropy loss function as shown in Equation 11 . The loss function constitutes two different parts, both of which define a different type of reaction centre. First, a reaction centre exists between a pair of atoms (ai, aj) if there is a bond type change during the reaction. This is usually encountered for bimolecular reactions. Mathematically, this is indicated by a binary variable yi,j, which assumes a value of 1 if the bond i, j ∈ E is changed. Second, a reaction centre is defined as any atom ai that experiences a change in the number of implicit hydrogen (unimolecular reaction). Equivalently, this is captured by binary variable yi for atom i ∈ N . Note that this definition only allows for the existence of one bond change, rather than multiple bond changes. This is however not a concern as all interpretability studies follow bimolecular reactions. Finally, a constraint is imposed on both binary variables as follows: