ReviewJournal of cheminformatics2014
Chemical named entities recognition: a review on approaches and applications.
Review in Journal of cheminformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
What it found
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Who cites it
39 citing papers in PubMed.
- A machine learning driven automated system to extract multiple information fields from safety data sheet documents.Heliyon · 2025Article
- SciLinker: a large-scale text mining framework for mapping associations among biological entities.Frontiers in artificial intelligence · 2025Article
- Chemical entity normalization for successful translational development of Alzheimer's disease and dementia therapeutics.Journal of biomedical semantics · 2024Article
- Application of machine reading comprehension techniques for named entity recognition in materials science.Journal of cheminformatics · 2024Article
- Zombie cheminformatics: extraction and conversion of Wiswesser Line Notation (WLN) from chemical documents.Journal of cheminformatics · 2024Article
- A Large Language Model Outperforms Other Computational Approaches to the High-Throughput Phenotyping of Physician Notes.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Distributed messaging and light streaming system for combating pandemics: A case study on spatial analysis of COVID-19 Geo-tagged Twitter dataset.Journal of ambient intelligence and humanized computing · 2023Article
- Large-scale application of named entity recognition to biomedicine and epidemiology.PLOS digital health · 2022Article
- MolMiner: You Only Look Once for Chemical Structure Recognition.Journal of chemical information and modeling · 2022Article
- Chemical named entity recognition in the texts of scientific publications using the naïve Bayes classifier approach.Journal of cheminformatics · 2022Article
- How can natural language processing help model informed drug development?: a review.JAMIA open · 2022Review
- Dataset of solution-based inorganic materials synthesis procedures extracted from the scientific literature.Scientific data · 2022Article
- Quantifying the advantage of domain-specific pre-training on named entity recognition tasks in materials science.Patterns (New York, N.Y.) · 2022Article
- Designing a multilayer film via machine learning of scientific literature.Scientific reports · 2022Article
- Enhanced neurologic concept recognition using a named entity recognition model based on transformers.Frontiers in digital health · 2022Article
- Review
- A review of optical chemical structure recognition tools.Journal of cheminformatics · 2020Review
- Integrated Random Negative Sampling and Uncertainty Sampling in Active Learning Improve Clinical Drug Safety Drug-Drug Interaction Information Retrieval.Frontiers in pharmacology · 2020Article
- Named Entity Recognition and Relation Detection for Biomedical Information Extraction.Frontiers in cell and developmental biology · 2020Review
- Representing Multiword Chemical Terms through Phrase-Level Preprocessing and Word Embedding.ACS omega · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid increase in the flow rate of published digital information in all disciplines has resulted in a pressing need for techniques that can simplify the use of this information. The chemistry literature is very rich with information about chemical entities. Extracting molecules and their related properties and activities from the scientific literature to "text mine" these extracted data and determine contextual relationships helps research scientists, particularly those in drug development. One of the most important challenges in chemical text mining is the recognition of chemical entities mentioned in the texts. In this review, the authors briefly introduce the fundamental concepts of chemical literature mining, the textual contents of chemical documents, and the methods of naming chemicals in documents. We sketch out dictionary-based, rule-based and machine learning, as well as hybrid chemical named entity recognition approaches with their applied solutions. We end with an outlook on the pros and cons of these approaches and the types of chemical entities extracted.
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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.