C-MAGE: Automated Extraction and Organization Engine for Molecular Representations in Chemical Documents
TL;DR
C-MAGE enables large-scale information extraction of chemical structures in a consistent and reproducible pipeline, complementing the more common text extractions, and led to a 7.1% increase in accurate molecular representations generated.
Abstract
To exploit the data present in chemical literature for AI applications in chemistry, an expanded and reliable extraction pipeline is needed. AI-ready chemical databases broaden chemical space coverage and provide benchmarking and training sets for machine learning model development to drive innovation within the field. To curate data from chemical documents, a multimodal approach is needed. C-MAGE is an open-source molecule extraction engine that processes figures and tables in PDF files to provide machine-readable molecule data sets of the structures as CXSMILES strings, an encompassing representation that evades heuristic translation of superatoms to SMILES subunits. This led to a 7.1% increase in accurate molecular representations generated. All data extracted undergo confidence-based sorting to ensure high precision (77.6%) in data set collection and remove the need for manually sorting data point validity. Both the CXSMILES and their confidence values are reproducible and are generated efficiently (1.7 s per molecule). C-MAGE’s use is demonstrated by the generation of an annotated data set containing 1454 CXSMILES strings from 38 chemical documents. C-MAGE thus enables large-scale information extraction of chemical structures in a consistent and reproducible pipeline, complementing the more common text extractions.