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.
@article{arxiv.2305.11845,
title = {RxnScribe: A Sequence Generation Model for Reaction Diagram Parsing},
author = {Yujie Qian and Jiang Guo and Zhengkai Tu and Connor W. Coley and Regina Barzilay},
journal= {arXiv preprint arXiv:2305.11845},
year = {2023}
}
Comments
To be published in the Journal of Chemical Information and Modeling