Forward prediction and single-step retrosynthesis models both need to predict how bonds might be broken and formed to produce new molecules. A common approach is to enumerate a set of templates for bond changes that happen in particular classes of reactions and use a classifier to predict the most likely template given a set of molecules . Alternatively, some models have been designed to explicitly predict bond changes . One promising approach is to directly predict the SMILES strings of the reactants (single-step retrosynthesis) or products (forward prediction) using a natural language processing model such as a transformer . In this work we use the transformer architecture of Schwaller et al.