In addition, we found that Ring Breaker trained on the Reaxys dataset outperformed that trained on the UPSTO dataset across the substrates in Figure (Figure and Figure ). The difference in performance may arise from the differing template space covered by the models as we have found in our previous studies. This is evidenced by the greater number of applicable templates that correspond to ring formations in the Reaxys dataset as found by exhaustive application (Figure and Figure ). Therefore, both the type and quantity of templates available to the Reaxys model differ from the USPTO model, as the two models did not differ in architecture, but in the template and training set used. The diversity of the products in the training set for each of the models could additionally influence how well the model is able to learn, however this has not been examined in this study.