The recent wave of artificial intelligence (AI) within drug discovery has heavily impacted in the fields of de novo design, synthesis planning, and bioactivity prediction to name a few. This holds the promise of accelerating Design, Make, Test, Analyze (DMTA) cycles, for which predictive models are desired to reduce failure rates in the drug discovery process. Computer aided synthesis planning (CASP) has long been investigated as a means for predicting how to make a given compound. However, despite recent Furthermore, the neural networks upon which CASP tools are built, are trained using the single label (templates) obtained from the dataset. As an analogy to retrosynthetic planning, this resembles a one compound to one reaction (template) situation, whereas in 'truth' a compound can be synthesized by multiple reactions at any given step in the pathway. In this study we propose a method for the extraction of multiple labels from the underlying dataset and demonstrate its use in the prediction of retrosynthetic ring disconnections. This was extended to the prediction of previously unseen fragments, such as the so called 'Rings of the Future' for which we examine the predictive performance. We show how 'Ring Breaker' can be viewed as a specialist for predicting ring formations, and used alongside current CASP tools, to guide route finding into pathways exploiting ring synthesis. The implications of predicting ring synthesis are far reaching and extend beyond the medicinal chemistry domain, to dyes, fragrances and agrochemicals to name a few.