To extract routes, we started from the subset of the USPTO dataset prepared by Thakkar et al. , as this is a dataset that contains atom-mapped reactions and reaction templates, which is necessary for training a template-based one-step retrosynthesis model. We kept only reactions for which the template occurrence was four or more, i.e. all the reactions in the dataset are represented by a reaction template that occurs at least four times in the dataset. This amounts to 867,620 reactions. When extracting the routes from this dataset, we left out 99,093 reactions, three examples for each unique reaction template, to be able to have sufficient data to train a one-step model on (see below). We will now detail how routes were extracted from the 768,527 remaining reactions, a procedure summarized in Figure . We extracted 1,046,088 routes from 80,639 patents using the method of Mo et al. , provided as a script but with an increased timeout for finding a route from 6 to 10 s. The script puts all the reactions from a patent in a reaction network (see an example in Figure ), identifies molecules that only exist as a product as the starting point for synthetic routes, and then uses a depth-first search to extract the routes. After extracting the routes, we immediately discarded all routes with a single leaf, to avoid uninteresting transformation sequences, resulting in 158,698 routes. The distributions of the number of molecules, leaves, reactions, and longest linear route (LLR) are shown in Figure . The dataset is tilted towards short routes with few leaves. Only 4,7% of the routes are convergent; the remainder are linear. We believe the 150K routes are too extensive to benchmark route predictions and it is likely that many routes from the same patent are similar. Therefore we processed the dataset further, although we acknowledge that the 150K routes could be used for e.g. machine learning tasks.