OpenChemIE: An Information Extraction Toolkit For Chemistry Literature
Vincent Fan Yujie Qian Alex Wang Amber Wang Connor W. Coley Regina Barzilay
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.