The extracted templates can be applied to a given compound to enumerate a set of outcomes/reactants that return the retrosynthetic options available. For a template to be applicable and generate a set of outcomes/reactants, there must be a substructure match between the template and queried compound. As not all templates have a substructure match with the queried compound, not all templates can be applied to any given compound. Therefore, it is necessary to predict which templates † Copyright © 2019 Elsevier Life Science IP Limited except certain content provided by third parties. Reaxys is a trademark of Elsevier Life Science IP Limited can be applied to prevent an exhaustive search across all templates. The problem of predicting retrosynthetic pathways is formally described as a tree search, whereby the templates are applied recursively to generate a tree of possible options. In practice, the number of templates applied is the top 50 predicted by the model. As mentioned previously, not all templates will yield a set of reactants, therefore the number of options at each step will be less than the 50 predicted. An additional constraint is applied such that when the cumulative sum of the probabilities associated with each template reaches a cut-off threshold, no further templates will be applied, therefore the number of options will be less than 50 in this instance. This limits the computational expense of enumerating all possible options given by the extracted templates. The tree is then searched to find the most efficient synthetic pathways. This study is focused on how prioritization of templates, specifically those that are associated with the formation of rings, can be used to the predict synthesis of ring systems. Having determined which templates have been used in the context of forming rings, we examine three approaches for prioritization and compare them: 1) prediction using a model trained on all templates extracted from the reaction data including those that do not form rings, to determine whether such a model is able to prioritize ring formations, termed the standard model. 2) applying a filter at inference to the standard model trained on all templates to select only those that correspond to ring formations, termed the filtered model. 3) a model trained on only the templates that have been used in the context of forming rings, termed 'Ring Breaker'. In each instance, the model architectures are held constant, and only the inference method or training data is varied. For each of the models we train on either the templates obtained from the Reaxys or USPTO datasets. We then examine and compare the predictive capability of selected models on a set of commonly used ring formations, fragments from the ZINC database, approved drugs from DrugBank, and two compounds from the 'rings of the future' set.