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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| _version_ | 1849330282528768000 |
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| author | Shubham Gupta Rafael Teixeira de Lima Lokesh Mishra Cesar Berrospi Panagiotis Vagenas Nikolaos Livathinos Christoph Auer Michele Dolfi Peter Staar |
| author_facet | Shubham Gupta Rafael Teixeira de Lima Lokesh Mishra Cesar Berrospi Panagiotis Vagenas Nikolaos Livathinos Christoph Auer Michele Dolfi Peter Staar |
| author_sort | Shubham Gupta |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3b92a23fe34547089ff9bce70801115c |
| institution | Kabale University |
| issn | 2689-5595 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied AI Letters |
| spelling | doaj-art-3b92a23fe34547089ff9bce70801115c2025-08-20T03:46:58ZengWileyApplied AI Letters2689-55952025-04-0162n/an/a10.1002/ail2.124ChemQuery: A Natural Language Query‐Driven Service for Comprehensive Exploration of Chemistry Patent LiteratureShubham Gupta0Rafael Teixeira de Lima1Lokesh Mishra2Cesar Berrospi3Panagiotis Vagenas4Nikolaos Livathinos5Christoph Auer6Michele Dolfi7Peter Staar8IBM Research Paris‐Saclay Orsay FranceIBM Research Paris‐Saclay Orsay FranceIBM Research Zurich Rüschlikon SwitzerlandIBM Research Zurich Rüschlikon SwitzerlandIBM Research Zurich Rüschlikon SwitzerlandIBM Research Zurich Rüschlikon SwitzerlandIBM Research Zurich Rüschlikon SwitzerlandIBM Research Zurich Rüschlikon SwitzerlandIBM Research Zurich Rüschlikon SwitzerlandABSTRACT 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.https://doi.org/10.1002/ail2.124chemistry patentsmolecule searchnatural language queriespatent searchquestion answering |
| spellingShingle | Shubham Gupta Rafael Teixeira de Lima Lokesh Mishra Cesar Berrospi Panagiotis Vagenas Nikolaos Livathinos Christoph Auer Michele Dolfi Peter Staar ChemQuery: A Natural Language Query‐Driven Service for Comprehensive Exploration of Chemistry Patent Literature Applied AI Letters chemistry patents molecule search natural language queries patent search question answering |
| title | ChemQuery: A Natural Language Query‐Driven Service for Comprehensive Exploration of Chemistry Patent Literature |
| title_full | ChemQuery: A Natural Language Query‐Driven Service for Comprehensive Exploration of Chemistry Patent Literature |
| title_fullStr | ChemQuery: A Natural Language Query‐Driven Service for Comprehensive Exploration of Chemistry Patent Literature |
| title_full_unstemmed | ChemQuery: A Natural Language Query‐Driven Service for Comprehensive Exploration of Chemistry Patent Literature |
| title_short | ChemQuery: A Natural Language Query‐Driven Service for Comprehensive Exploration of Chemistry Patent Literature |
| title_sort | chemquery a natural language query driven service for comprehensive exploration of chemistry patent literature |
| topic | chemistry patents molecule search natural language queries patent search question answering |
| url | https://doi.org/10.1002/ail2.124 |
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