RxnScribe: A Sequence Generation Model for Reaction Diagram Parsing
Yujie Qian Jiang Guo Zhengkai Tu Connor W. Coley Regina Barzilay
Reaction diagram parsing is the task of extracting reaction schemes from a
diagram in the chemistry literature. The reaction diagrams can be arbitrarily
complex, thus robustly parsing them into structured data is an open challenge.
In this paper, we present RxnScribe, a machine learning model for parsing
reaction diagrams of varying styles. We formulate this structured prediction
task with a sequence generation approach, which condenses the traditional
pipeline into an end-to-end model. We train RxnScribe on a dataset of 1,378
diagrams and evaluate it with cross validation, achieving an 80.0% soft match
F1 score, with significant improvements over previous models. Our code and data
are publicly available at https://github.com/thomas0809/RxnScribe.