ArticleJournal of chemical information and modeling2023
ReactionDataExtractor 2.0: A Deep Learning Approach for Data Extraction from Chemical Reaction Schemes.
Article in Journal of chemical information and modeling, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Data-Driven Materials Science for Energy-Sustainable Applications.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Transforming Molecular Synthesis With Large Language Models.Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Review
- ReactionSeek: LLM-powered literature data mining and knowledge discovery in organic synthesis.Nature communications · 2026Article
- Metabolite Identification Data in Drug Discovery, Part 1: Data Generation and Trend Analysis.Molecular pharmaceutics · 2025Article
- Suitability of large language models for extraction of high-quality chemical reaction dataset from patent literature.Journal of cheminformatics · 2024Article
- EnzChemRED, a rich enzyme chemistry relation extraction dataset.Scientific data · 2024Article
- Fine-tuning large language models for chemical text mining.Chemical science · 2024Article
- Text Mining the Literature to Inform Experiments and Rationalize Impurity Phase Formation for BiFeOChemistry of materials : a publication of the American Chemical Society · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Knowledge in the chemical domain is often disseminated graphically via chemical reaction schemes. The task of describing chemical transformations is greatly simplified by introducing reaction schemes that are composed of chemical diagrams and symbols. While intuitively understood by any chemist, like most graphical representations, such drawings are not easily understood by machines; this poses a challenge in the context of data extraction. Currently available tools are limited in their scope of extraction and require manual preprocessing, thus slowing down the speed of data extraction. We present a new tool, ReactionDataExtractor v2.0, which uses a combination of neural networks and symbolic artificial intelligence to effectively remove this barrier. We have evaluated our tool on a test set composed of reaction schemes that were taken from open-source journal articles and realized F1 score metrics between 75 and 96%. These evaluation metrics can be further improved by tuning our object-detection models to a specific chemical subdomain thanks to a data-driven approach that we have adopted with synthetically generated data. The system architecture of our tool is modular, which allows it to balance speed and accuracy to afford an autonomous, high-throughput solution for image-based chemical data extraction.
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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.