Schwaller et al. have also shown that the attention matrix weights of transformers that have been trained on unlabelled chemical reaction data can be used to determine accurate atom mappings . Additionally, they demonstrated that attention-based models are highly suitable for the classification of chemical reactions . Similar model architectures were successfully used to generate specific synthesis instructions and to determine the yield of a given chemical reaction formula . Andronov et al. successfully demonstrated the prediction of reagents based on given reaction SMILES strings using transformers. They were then able to use the reagent prediction model to fill in missing reagents in incomplete reaction data from US patents leading to an improved state-of-the-art model for the prediction of reaction products . Recently, Rohrbach et al. demonstrated the translation of synthesis protocols in the literature into a standardized chemical language, which could then be executed by their automated synthesis system .