ChemQuery: A Natural Language Query‐Driven Service for Comprehensive Exploration of Chemistry Patent Literature
ABSTRACT Patents are integral to our shared scientific knowledge, requiring companies and inventors to stay informed about them to conduct research, find licensing opportunities, and manage legal risks. However, the rising rate of filings has made this task increasingly challenging over the years. T...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-04-01
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| Series: | Applied AI Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/ail2.124 |
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| Summary: | ABSTRACT Patents are integral to our shared scientific knowledge, requiring companies and inventors to stay informed about them to conduct research, find licensing opportunities, and manage legal risks. However, the rising rate of filings has made this task increasingly challenging over the years. To address this issue, we introduce ChemQuery, a tool for easily exploring chemistry‐related patents using natural language questions. Traditional systems rely on simplistic keyword‐based searches to find patents that might be relevant to a user's request. In contrast, ChemQuery uses up‐to‐date information to return specific answers, along with their sources. It also offers a more comprehensive search experience to the users, thanks to capabilities like extracting molecules from diagrams, integrating information from PubChem, and allowing complex queries about molecular structures. We conduct a thorough empirical evaluation of ChemQuery and compare it with several baseline approaches. The results highlight the practical utility and limitations of our tool. |
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| ISSN: | 2689-5595 |