On the public test datasets, this resulted in 48,628 fragments with an average of 27 and a maximum of 70 missing atoms (Figure ). On such fragments, the model obtained an accuracy of 96.3% when it was tasked to complete fragments missing up to 10 atoms, 71.5% for fragments missing up to 30 atoms, and 35.4% for fragments beyond 30 (Figure ). Nonetheless, for these fragments the average Tanimoto of the predicted molecules was 0.82 (Figure ) and in 73.0% of the cases the Tanimoto similarity was at least 0.675, indicating a close agreement to the ground truth. Despite these encouraging results, the molecular structures under consideration were entirely new for the model and never seen during training. Drug substance impurities however tend to be structurally similar among each other; during the development and optimization process of the synthesis pathway a significant number of related impurities are characterized, and several distinct projects make use of similar reactions to synthesize the respective main compounds. These considerations motivated us to make use of this historical data and investigate ways to incorporate this implicit process knowledge into the model.