After training the model, we automated a dataset creation procedure mimicking the endpoints of reaction processes such as hydrolysis, diels-alder, and more. The procedure works as follows. First, we automate the identification of reactant functional groups across the entire QM9 using a specified reactant label. The automation procedure can annotate every atom's environment up to any compositional depth (one, two, to several bonds away) in a bijective labelling representation using atomic number priority to order neighboring atoms ensuring the uniqueness of the label. This is done in a way very similar to well-established standards of ordering R/S or E/Z nomenclature. Ultimately, we obtain a local-centric label for each atom in the dataset that can be queried for long-range features, for instance, straight-chain alcohols, or alpha-positioned alkynes, or any other branching motif specified, or left unspecified (ex. all alcohols in QM9). Second, once reactants are isolated, a specified reaction is automated on the dataset. For instance, if the specified reaction is a methylation, this is carried out by removing a hydrogen and adding a methyl to mimic methylation on the alcohols. Lastly, we geometry optimize using RDKit's force field, MMFF94. Following the preparation of the reactant and product databases, we then run the reactants and products through the pretrained GNN model to extract their atom-embeddings at the reaction-site. For instance, in hydrolysis, this would be the carbon on the carbonyl group. This is all automated in a python workflow visualised in Figure . The software for this procedure is open source and is referenced in the Supplementary Information section.