Reaction datasets, in their current form, contain records of individual reactions whereby one compound can be the product of several different reaction classes or combination of reactants. In previous approaches these individual records have been used to train neural networks to either predict a retrosynthetic step or for reaction prediction. However, this strategy neglects the one to many nature of retrosynthetic analysis, where a given compound be constructed in more than one way.