Using OC|processor to discover new PFAS molecules from publications
We are glad that our new joint publication with Universtity of Luxemburg is available as a preprint
The extraction of chemical information from documents is a demanding task in cheminformatics due to the variety of text and image-based representations of chemistry. The present work describes the extraction of chemical compounds with unique chemical structures from the open access CORE (COnnecting REpositories) and Google Patents full text document repositories. The importance of structure normalization is demonstrated using three open access cheminformatics toolkits: CDK, RDKit and OpenChemLib. Each toolkit was used for structure parsing, normalization and subsequent substructure searching, using SMILES as structure representations of chemical molecules. Per- and polyfluoroalkyl substances (PFAS) were chosen as a case study to perform the substructure search, due to their high environmental relevance, their presence in both literature and patent corpuses, and the current lack of community consensus on their definition. Three different structural definitions of PFAS were chosen to highlight the implications of various definitions from a cheminformatics perspective. Since CDK, RDKit and OpenChemLib implement different criteria and methods for SMILES parsing and normalization, different numbers of parsed compounds were extracted, which were then evaluated using the three PFAS definitions. A comparison of these toolkits and definitions is provided, along with a discussion of the implications for PFAS screening and text mining efforts in cheminformatics. Finally, the extracted PFAS (~1.7 M PFAS from patents and ~27K from CORE) were compared against various existing PFAS lists and are provided in various formats for further community research efforts.