RxnScribe: A Sequence Generation Model for Reaction Diagram Parsing
Name
2305.11845v1.pdf
Description
Accepted version
Size
3.31 MB
Format
Adobe PDF
Checksum (MD5)
88beebeca5f2db019eb1e05966bcf072
Author(s)
Qian, Yujie
Guo, Jiang
Tu, Zhengkai
Coley, Connor W
Barzilay, Regina
Date Issued
July 10, 2023
Journal
Journal of Chemical Information and Modeling
Publisher
American Chemical Society (ACS)
Citation
Yujie Qian, Jiang Guo, Zhengkai Tu, Connor W. Coley, and Regina Barzilay. Journal of Chemical Information and Modeling 2023 63 (13), 4030-4041.
Version
Author's final manuscript
Abstract
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 1378 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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-ShareAlike
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DOI of Published Version
https://doi.org/10.1021/acs.jcim.3c00439
