We have developed a methodology for proposing syntheses and assessing the synthetic accessibility of ring systems using a specialized ring-forming neural network called 'Ring Breaker'; remarkably, our 'Ring Breaker' can predict more templates in the top 50 predictions than the standard and filtered models. It can be used either as a stand-alone tool, or it can be incorporated into synthetic planning tools. In addition, we have described a scalable and representative method for the generation of labels for computer aided synthesis planning tasks. The model, when trained separately on either the USPTO or Reaxys datasets, shows that the model trained on Reaxys outperforms that trained on the USPTO dataset. Notably, in cases where one fails the other is often able to suggest a suitable disconnection, and in this sense the two datasets are complementary. We determined that for a series of common ring formations and ring fragment subsets obtained from the ZINC database, the models can suggest suitable disconnections for the ring-forming step in most cases. Furthermore, in all subsets examined 'Ring Breaker' outperforms the predictive capability of the standard and filtered models. This is because the Although the models consistently underperform for ring systems smaller than 5 heavy atoms, for which fragments are not common because of torsional strain. The distribution of compounds for which ringforming steps could be predicted closely follows the raw distribution of ring systems in the subsets. Our study was extended to previously unseen ring systems, for which the model trained solely on the USPTO dataset could predict the ring-forming step reported in the literature as the first applicable template. This highlights the utility of the method across the range of common, rare, and previously unseen ring systems, where the tool can be used as an idea generator. Given that the model varies in predictive capability depending on the substitution of the ring system, we established that this originates from the availability of a suitable template and by association the underlying dataset. Whilst suitable templates describing the reaction are suggested, they cannot be applied as they do not share an exact substructure match to the query compound. To verify that the 'Ring Breaker' model is predictive on full structures and not only fragments, we applied the model to 2039 approved drugs from DrugBank. The 'Ring Breaker' model trained on the Reaxys dataset was able to predict more ring formations in the top 50 predictions, and with a lower rank than the corresponding USPTO model.