Curating Reagents in Chemical Reaction Data with an Interactive Reagent Space Map

The increasing use of machine learning and artificial intelligence in chemical reaction studies demands high-quality reaction data, necessitating specialized tools enabling data understanding and curation. Our work introduces a novel methodology for reaction data examination centered on reagents - essential molecules in reactions that do not contribute atoms to products. We propose an intuitive tool for creating interactive reagent space maps using distributed vector representations, akin to word2vec in Natural Language Processing, capturing the statistics of reagent usage within datasets. Our approach enables swift assessment of reagent action patterns and identification of erroneous reagent entries, which we demonstrate using the USPTO dataset. Our contributions include an open-source web application for visual reagent pattern analysis and a table cataloging around six hundred of the most frequent reagents in USPTO annotated with detailed roles. Our method aims to support organic chemists and cheminformatics experts in reaction data curation routine.