We performed a one-step retrosynthetic analysis considering only the top 50 predictions, to focus on the ring-forming step required to synthesize a range of ring containing subsets from the ZINC database (Figure ). Examining a range of ring systems, from the most commonly occurring (in > 100 K substances) to the rarest (in < 1 K substances), we found that 'Ring Breaker' was able to predict ring formations for more substrates than that of the standard models across all subsets examined, regardless of the dataset used. The reason for this may be two-fold. First 'Ring Breaker' is exclusively limited to ring formations or reactions for which ring formations may result depending on context, so application of a promiscuous template may still lead to a result. However, this alone is not likely to lead to the large difference in performance observed. Second, the limited and domain specific training set better allows the model to learn in which context ring-forming templates can be predicted without distractions from non-ring forming templates. Therefore, it can prioritize the ring forming templates on which it is trained better than the standard model. This was found by filtering the predictions from the standard model for ring formations, as demonstrated in Figure . This is in comparison to the standard model in which ringforming templates can be drowned out in the noise by more frequently-occurring templates, as there are several possible options for disconnections aside from the ring-forming templates.