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 in which
enzymes and the chemical reactions they catalyze are annotated using
identifiers from the UniProt Knowledgebase (UniProtKB) and the ontology of
Chemical Entities of Biological Interest (ChEBI). We show that fine-tuning
pre-trained language models with EnzChemRED can significantly boost their
ability to identify mentions of proteins and chemicals in text (Named Entity
Recognition, or NER) and to extract the chemical conversions in which they
participate (Relation Extraction, or RE), with average F1 score of 86.30% for
NER, 86.66% for RE for chemical conversion pairs, and 83.79% for RE for
chemical conversion pairs and linked enzymes. We combine the best performing
methods after fine-tuning using EnzChemRED to create an end-to-end pipeline for
knowledge extraction from text and apply this 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. The EnzChemRED corpus is
freely available at this https URL

通过 EnzChemRED 数据集，利用大型语言模型等自然语言处理方法可以显著提高识别文本中的蛋白质和化学品提及以及提取它们参与的化学转化的能力，该数据集位于 PubMed 摘要中，旨在支持酶的修饰。