Posted on April 3rd, 2019 by Saber A. Akhondi in Pharma R&D
Researchers regularly turn to scholarly literature for information to help them in their R&D efforts. To learn about new chemical compounds, scientists not only look at peer-reviewed journals, but often must also search patents. Products such as Elsevier’s Reaxys make it possible for them to search through a multitude of these articles and patents quickly to find the information they need.
Text-mining technology has enabled Elsevier to expand its patent-searching capabilities, by closing the gap between state-of-the-art automated approaches and traditional high quality but difficult to scale manual processing. In Elsevier, our team of NLP, ML, and chemoinformatic experts continue to develop our automation processes so that researchers are better able to zero in on the data that is most valuable to them.
Case in point is a new study conducted by the Elsevier Data Science team, which has been well received by the research community. Published in the journal Database, the paper Automatic identification of relevant chemical compounds from patents reveals an automated system that specifically identifies relevant compounds.
“In this study, we design an automated system that extracts chemical entities from patents and classifies their relevance,” explains the paper’s abstract, noting the study’s favorable results. “Our system can extract chemical compounds from patents and classify their relevance with high performance.”
To learn more, read the complete paper here.
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