ArticleJournal of cheminformatics2015
CHEMDNER: The drugs and chemical names extraction challenge.
Article in Journal of cheminformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers.
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Who cites it
69 citing papers in PubMed.
- Harnessing AI to fuse phenotypic signatures for drug target identification: progress in computational modeling.Briefings in bioinformatics · 2026Review
- A Systematic Review of Drug-Related InteractionsUtilizing Deep Learning and LLMs for Prediction and Mitigation.ACS omega · 2025Review
- Benchmarking large language models for personalized, biomarker-based health intervention recommendations.NPJ digital medicine · 2025Article
- Clinical insights: A comprehensive review of language models in medicine.PLOS digital health · 2025Review
- A machine learning driven automated system to extract multiple information fields from safety data sheet documents.Heliyon · 2025Article
- Taiyi: a bilingual fine-tuned large language model for diverse biomedical tasks.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Integrating deep learning architectures for enhanced biomedical relation extraction: a pipeline approach.Database : the journal of biological databases and curation · 2024Article
- Vocabulary Matters: An Annotation Pipeline and Four Deep Learning Algorithms for Enzyme Named Entity Recognition.Journal of proteome research · 2024Article
- ShinyTPs: Curating Transformation Products from Text Mining Results.Environmental science & technology letters · 2023Article
- Large-Scale Biomedical Relation Extraction Across Diverse Relation Types: Model Development and Usability Study on COVID-19.Journal of medical Internet research · 2023Article
- Chemical identification and indexing in full-text articles: an overview of the NLM-Chem track at BioCreative VII.Database : the journal of biological databases and curation · 2023Article
- CafeteriaSA corpus: scientific abstracts annotated across different food semantic resources.Database : the journal of biological databases and curation · 2022Article
- Challenges and opportunities for mining adverse drug reactions: perspectives from pharma, regulatory agencies, healthcare providers and consumers.Database : the journal of biological databases and curation · 2022Article
- Chemical named entity recognition in the texts of scientific publications using the naïve Bayes classifier approach.Journal of cheminformatics · 2022Article
- New reasons for biologists to write with a formal language.Database : the journal of biological databases and curation · 2022Article
- Natural language processing models that automate programming will transform chemistry research and teaching.Digital discovery · 2022Review
- MetaboListem and TABoLiSTM: Two Deep Learning Algorithms for Metabolite Named Entity Recognition.Metabolites · 2022Article
- DECIMER 1.0: deep learning for chemical image recognition using transformers.Journal of cheminformatics · 2021Article
- Extracting Drug Names and Associated Attributes From Discharge Summaries: Text Mining Study.JMIR medical informatics · 2021Article
- NLM-Chem, a new resource for chemical entity recognition in PubMed full text literature.Scientific data · 2021Article
9 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Natural language processing (NLP) and text mining technologies for the chemical domain (ChemNLP or chemical text mining) are key to improve the access and integration of information from unstructured data such as patents or the scientific literature. Therefore, the BioCreative organizers posed the CHEMDNER (chemical compound and drug name recognition) community challenge, which promoted the development of novel, competitive and accessible chemical text mining systems. This task allowed a comparative assessment of the performance of various methodologies using a carefully prepared collection of manually labeled text prepared by specially trained chemists as Gold Standard data. We evaluated two important aspects: one covered the indexing of documents with chemicals (chemical document indexing - CDI task), and the other was concerned with finding the exact mentions of chemicals in text (chemical entity mention recognition - CEM task). 27 teams (23 academic and 4 commercial, a total of 87 researchers) returned results for the CHEMDNER tasks: 26 teams for CEM and 23 for the CDI task. Top scoring teams obtained an F-score of 87.39% for the CEM task and 88.20% for the CDI task, a very promising result when compared to the agreement between human annotators (91%). The strategies used to detect chemicals included machine learning methods (e.g. conditional random fields) using a variety of features, chemistry and drug lexica, and domain-specific rules. We expect that the tools and resources resulting from this effort will have an impact in future developments of chemical text mining applications and will form the basis to find related chemical information for the detected entities, such as toxicological or pharmacogenomic properties.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.