EnzChemRED, a rich enzyme chemistry relation extraction dataset
Expert curation is essential to capture knowledge of enzyme functions from the scientific literature in FAIR open knowledgebases but cannot keep pace with the rate of new discoveries and new publications. In this work we present EnzChemRED, for Enzyme Chemistry Relation Extraction Dataset, a new training and benchmarking dataset to support the development of Natural Language Processing (NLP) methods such as (large) language models that can assist enzyme curation. EnzChemRED consists of 1,210 expert curated PubMed abstracts where enzymes and the chemical reactions they catalyze are annotated using identifiers from the protein knowledgebase UniProtKB and the chemical ontology ChEBI. We show that fine-tuning language models with EnzChemRED significantly boosts their ability to identify proteins and chemicals in text (86.30% F<sub>1</sub> score) and to extract the chemical conversions (86.66% F<sub>1</sub> score) and the enzymes that catalyze those conversions (83.79% F<sub>1</sub> score). We apply our methods to abstracts at PubMed scale to create a draft map of enzyme functions in literature to guide curation efforts in UniProtKB and the reaction knowledgebase Rhea.
Authors
- Po‐Ting Lai (ORCID: https://orcid.org/0000-0003-2025-318X)
- Edouard de Castro (ORCID: https://orcid.org/0000-0001-5436-7383)
- Ivo Pedruzzi (ORCID: https://orcid.org/0000-0001-8561-7170)
- Marc Feuermann
- Catherine Rivoire (ORCID: https://orcid.org/0000-0002-5979-8382)
- Robert Leaman (ORCID: https://orcid.org/0000-0003-3296-5766)
- Lionel Breuza (ORCID: https://orcid.org/0000-0002-8075-8625)
- Lucille Pourcel (ORCID: https://orcid.org/0000-0003-1522-9900)
- Nicole Redaschi (ORCID: https://orcid.org/0000-0001-8890-2268)
- Chih-Hsuan Wei (ORCID: https://orcid.org/0000-0001-5094-7321)
- Anastasia Sveshnikova (ORCID: https://orcid.org/0000-0001-9291-0965)
- Lucila Aimo (ORCID: https://orcid.org/0000-0003-0943-6401)
- Kristian B. Axelsen (ORCID: https://orcid.org/0000-0003-3889-2879)
- Sylvain Poux (ORCID: https://orcid.org/0000-0001-7299-6685)
- Zhiyong Lu (ORCID: https://orcid.org/0000-0001-9998-916X)
- Elisabeth Coudert (ORCID: https://orcid.org/0000-0001-8314-404X)
- Alan Bridge (ORCID: https://orcid.org/0000-0003-2148-9135)
- Anne Morgat (ORCID: https://orcid.org/0000-0002-1216-2969)
- Ling Luo (ORCID: https://orcid.org/0000-0002-5141-0259)
Institutions
- SIB Swiss Institute of Bioinformatics (CH)
- National Institutes of Health (US)
- Dalian University of Technology (CN)
- National Center for Biotechnology Information (US)
Publication Details
- Journal
- Scientific Data
- Published
- 2024-09-09
- DOI
- https://doi.org/10.1038/s41597-024-03835-7
- Citations
- 8
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 1.87
Funders
- Australian Government
- European Commission
- National Institutes of Health
- HORIZON EUROPE Framework Programme
- Staatssekretariat für Bildung, Forschung und Innovation
- Fundamental Research Funds for the Central Universities
- National Institute on Aging
- National Heart, Lung, and Blood Institute
- National Human Genome Research Institute
- National Eye Institute
- National Cancer Institute
- National Institute of General Medical Sciences
- National Institute of Allergy and Infectious Diseases
- National Institute of Diabetes and Digestive and Kidney Diseases
- U.S. National Library of Medicine