Random oversampling/undersampling. As an alternative to adding information on the atomic level, we also tested oversampling of seldomly used templates. The reason for oversampling/undersampling in machine learning is that classifiers (as the recommender model) are typically more sensitive towards the majority class. This can be illustrated by analyzing the mean template occurrence, which describes the average reported instances of the templates used/predicted of the reactions in question. For example, two reactions rely on templates reported 50 and 100 times, resulting in a mean template occurrence of 75. In the USPTO test set, the mean template occurrence is 1112, while the top-1 mean template occurrence for the GCN (RDKit) model is 1742. This indicates that the trained model generally predicts more frequently used templates than the underlying dataset dictates. To overcome the bias, more weight can be assigned to the minority classes in the training set by sampling them with replacement or reducing the importance of the majority classes in training set by undersampling, which means that you only choose some samples from the majority class. The objective of oversampling/undersampling is to get a more even accuracy across all types of reactions, which would be a more horizontal line for the cumulative template occurrence in Figure .