Automatic identification of relevant chemical compounds from patents. (30th January 2019)

• Record Type: Journal Article
• Authors: Akhondi, Saber A; Rey, Hinnerk; Schwörer, Markus; Maier, Michael; Toomey, John; Nau, Heike; Ilchmann, Gabriele; Sheehan, Mark; Irmer, Matthias; Bobach, Claudia; Doornenbal, Marius; Gregory, Michelle; Kors, Jan A
• Is Part Of: Database. Volume 2019(2019)
• Publication Date: 2019-01-30
• Subjects: Biology -- Databases -- Periodicals; Bioinformatics -- Periodicals; 570.285
• DOI: 10.1093/database/baz001
• Languages: English
• ISSNs: 1758-0463

Abstract

Abstract: In commercial research and development projects, public disclosure of new chemical compounds often takes place in patents. Only a small proportion of these compounds are published in journals, usually a few years after the patent. Patent authorities make available the patents but do not provide systematic continuous chemical annotations. Content databases such as Elsevier's Reaxys provide such services mostly based on manual excerptions, which are time-consuming and costly. Automatic text-mining approaches help overcome some of the limitations of the manual process. Different text-mining approaches exist to extract chemical entities from patents. The majority of them have been developed using sub-sections of patent documents and focus on mentions of compounds. Less attention has been given to relevancy of a compound in a patent. Relevancy of a compound to a patent is based on the patent's context. A relevant compound plays a major role within a patent. Identification of relevant compounds reduces the size of the extracted data and improves the usefulness of patent resources (e.g. supports identifying the main compounds). Annotators of databases like Reaxys only annotate relevant compounds. In this study, we design an automated system that extracts chemical entities from patents and classifies their relevance. The gold-standard set contained 18 789 chemical entity annotations. Of these, 10% were relevant compounds, 88% were irrelevant and 2% were equivocal. Our compound recognition system was based on proprietary tools. The performance (F-score) of the system on compound recognition was 84% on the development set and 86% on the test set. The relevancy classification system had an F-score of 86% on the development set and 82% on the test set. Our system can extract chemical compounds from patents and classify their relevance with high performance. This enables the extension of the Reaxys database by means of automation.