In Equation , x T i and x T j refer to the updated node features after T layers of message passing. The edge features x T i,j are calculated from the concatenation of the absolute difference and sum of node features. Finally, both edge (zi,j) and node (zi) logits are calculated through a softmax layer after being transformed by a shallow MLP. The predicted reaction centre k * is then determined as the argument maximum of z. Once a precise model has been successfully trained, the subsequent challenge is to pinpoint the critical nodes responsible for identifying the reaction centre. This task is accomplished using a graph masking technique called GNNExplainer . GNNExplainer is designed to discern the subgraph GS that has the most significant impact on predicting a specific node or edge, in this context, the reaction centre (as illustrated in Figure ). The GNNExplainer optimises the subgraph search by maximising the mutual information (MI) between the model's prediction based on GS and the prediction based on the entire graph G through: