Information extraction from chemistry literature is vital for constructing
up-to-date reaction databases for data-driven chemistry. Complete extraction
requires combining information across text, tables, and figures, whereas prior
work has mainly investigated extracting reactions from single modalities. In
this paper, we present OpenChemIE to address this complex challenge and enable
the extraction of reaction data at the document level. OpenChemIE approaches
the problem in two steps: extracting relevant information from individual
modalities and then integrating the results to obtain a final list of
reactions. For the first step, we employ specialized neural models that each
address a specific task for chemistry information extraction, such as parsing
molecules or reactions from text or figures. We then integrate the information
from these modules using chemistry-informed algorithms, allowing for the
extraction of fine-grained reaction data from reaction condition and substrate
scope investigations. Our machine learning models attain state-of-the-art
performance when evaluated individually, and we meticulously annotate a
challenging dataset of reaction schemes with R-groups to evaluate our pipeline
as a whole, achieving an F1 score of 69.5%. Additionally, the reaction
extraction results of \ours attain an accuracy score of 64.3% when directly
compared against the Reaxys chemical database. We provide OpenChemIE freely to
the public as an open-source package, as well as through a web interface.

OpenChemIE 是一种用于从化学文献中提取信息的开源工具，通过结合文本、表格和图像等多个模态来提取反应数据，并使用专门的神经模型和化学知识算法来实现高效的信息提取和集成。