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Reaction diagram parsing is the task of extracting reaction schemes from adiagram in the chemistry literature. The reaction diagrams can be arbitrarilycomplex, thus robustly parsing them into structured data is an open challenge.In this paper, we present RxnScribe, a machine learning model for parsingreaction diagrams of varying styles. We formulate this structured predictiontask with a sequence generation approach, which condenses the traditionalpipeline into an end-to-end model. We train RxnScribe on a dataset of 1,378diagrams and evaluate it with cross validation, achieving an 80.0% soft matchF1 score, with significant improvements over previous models. Our code and dataare publicly available at https://github.com/thomas0809/RxnScribe.

RxnScribe: A Sequence Generation Model for Reaction Diagram Parsing