Furthermore, we found that the Reaxys 'Ring Breaker' outperformed that trained on the USPTO dataset (Figure ). This is in contrast to our previous observations, where we reported that the ability to generate synthetic routes for the standard model did not depend on the training dataset. We have now determined that for the domain specific case of ring formations there is a clear effect arising from the training set used thus template space, attributed to the number and diversity of the samples available to the network for training. The difference in performance between the 'Ring Breaker' models trained on the USPTO and Reaxys dataset can in part be attributed to the prevalence of some fragments in the Reaxys training set (SI_6). The raw dataset distribution is the distribution of compounds with a given heavy atom count as found for each ZINC subset. The distributions from each model show the distribution of compounds remaining after prediction with a given heavy atom count.