Mining patents using molecular similarity search

Links to Files

https://pubmed.ncbi.nlm.nih.gov/17990501/

Permanent Link

http://hdl.handle.net/11603/38579

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UMBC Information Systems Department

Author/Creator

Author/Creator ORCID

Date

2006-12

Type of Work

conference papers and proceedings
Text

Department

Program

Citation of Original Publication

Rhodes, James, Stephen Boyer, Jeffrey Kreulen, Ying Chen, and Patricia Ordonez. “Mining Patents Using Molecular Similarity Search.” In Biocomputing 2007, 304–15. WORLD SCIENTIFIC, 2006. https://doi.org/10.1142/9789812772435_0029.

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This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.

Subjects

Chemical Similarity
Patents
InChI
Search Engine
Data Mining

Abstract

Text analytics is becoming an increasingly important tool used in biomedical research. While advances continue to be made in the core algorithms for entity identification and relation extraction, a need for practical applications of these technologies arises. We developed a system that allows users to explore the US Patent corpus using molecular information. The core of our system contains three main technologies: A high performing chemical annotator which identifies chemical terms and converts them to structures, a similarity search engine based on the emerging IUPAC International Chemical Identifier (InChI) standard, and a set of on demand data mining tools. By leveraging this technology we were able to rapidly identify and index 3,623,248 unique chemical structures from 4,375,036 US Patents and Patent Applications. Using this system a user may go to a web page, draw a molecule, search for related Intellectual Property (IP) and analyze the results. Our results prove that this is a far more effective way for identifying IP than traditional keyword based approaches.