where reactants and products are SMILES strings. For En-zymeMap, this led to 21k reactions from 5.1k reactants. For MetAMDB, 15k reactions from 3.3k reactants were obtained. For RHEA, 7.6k reactions from 1.9k reactants were obtained. All datasets were split randomly into 80% training, 10% validation and 10% test data. We note that the models did not make use of the EC class or protein information, and therefore only learn which reaction outcomes are more likely in a general sense, and not specific to a protein. Reactions with different outcomes based on the EC class or protein identity thus add noise to the dataset, so that even a perfect model cannot reach 100% accuracy. We did not include the EC class or protein sequence, since recent work identified ma-jor shortcomings in current approaches to encode the protein information in a meaningful way even for highly curated data. We then trained a classification model on the full training sets, as well as random subsamples of differing sizes. We chose the graph-convolutional neural network architecture Chemprop with reaction support from Ref. 73 (CGR-Chemprop) for our classification models, since this framework was recently demonstrated to learn high-accuracy reaction properties such as barrier heights, rates and regioselectivities. CGR-Chemprop relies on transforming reaction SMILES to their corresponding condensed graph of reaction, an overlay between the reactant and product graphs, i.e. the artificial, graph-based transition state of the reaction. We trained for 100 epochs and used 10-fold cross-validation for each prediction task since the performance is very sensitive to the data split. All other hyperparameters such as number and size of layers, or learning rates were kept at their default values (three round of message passing with hidden size of 300, mean aggregation over the graph, 2 feed-forward layers with a hiden size of 300). We report the flat accuracy of the classification, i.e. the model's ability to discern between reactive and non-reactive data points. For example, if the classification model predicted [1 1 0 0 1] for the test set