We train the GCN (RDKit) model on three different training sets to test how random under-/oversampling influences the recommender models performance. The three different training sets all have varying levels of oversampling and undersampling. The first training set is created by oversampling. We sample ten reactions (with replacement) for each template class that occur ten or fewer times in the training set (35,355 of the 46,673 templates). The training set contains 1,022,940 reactions. The second training set is sampled identically to the first one, but instead, we use a cut-off of 50 (44,636 of the 46,673) occurrences. With a cut-off of 50, we get a training set with 2,717,960 reactions. The third training set is curated by both under-and oversampling the reactions, so the number of reactions in each template class is equal. We do this by sampling 30 reactions, with replacement, from each template class, resulting in a training set size of 1,400,190 reactions.