During the P2S step, the authors utilise a GNN to estimate the reactivity between pairs of atoms. The reaction centre is obtained as the pair of atoms with the highest reactivity score generating synthons. The synthons are completed by sequentially attaching individual atoms until the termination criterion is reached. As G2G only considers atom pairs as reaction centres, the model can only predict bond formations. Chen et al. improved upon the definition of the reaction centre. Their framework (G 2 Retro) captures changes in atom charges and induced bond type changes in addition to bond formations. In the S2R stage, G 2 Retro completes the synthons attaching molecular substructures instead of singular atoms. This renders the synthon completion process simpler given that leaving groups often consist of multiple functional groups (and thus atoms). G2G and G 2 Retro are both constrained to bimolecular reaction. Yan et al. extended their framework (RetroXpert) to trimolecular reactions. Moreover, they employ the Transformer for the S2R step, to overcome the challenging graph generation problem tackled by G2G. Akin to template classification, Somnath et al. pre-compiled a library of leaving groups. Thus, for each synthon, the most probable leaving group is selected from the library employing a GNN (GraphRetro). The downside to the two-step approach comes from the incorporation of two separate models for the two stages. These models cannot be trained jointly and an error made by the first model subsequently propagates through to the second model. For this reason, Sacha et al. conceived a single framework (MEGAN) that combines both P2S and S2R steps. As seen in Figure , the molecular graph is introduced to a GNN encoder that creates a latent feature space. From this feature space, the GNN decoder selects actions to directly perform graph edits on the molecule. The authors constrained their action space to 4 different tokens: EditAtom, EditBond, AddAtom, and AddBenzene. Finally, the model stops sampling from the action space once the termination token is predicted. To construct a leaving group, MEGAN must output the AddAtom tokens numerous times, rendering the sequence of actions long and inefficient. Liu et al. and Zhong et al. proposed to add substructures to the synthons to shorten the sequence. Both frameworks (MARS and Graph2Edits), achieve an improvement in the top-k accuracy over MEGAN. In the case study, we include MEGAN as a baseline for the sequential graph edit approach.