ReviewJournal of cheminformatics2022
Review of techniques and models used in optical chemical structure recognition in images and scanned documents.
Review in Journal of cheminformatics, 2022. 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.
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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.
- A Benchmark Evaluation of Chemical Structure Extraction from Patents: Insights and Challenges in Chemical Structure Recognition.Chemical research in toxicology · 2026Article
- Efficient fine-tuning of vision-language adapters in chemical VLMs for molecular image-text tasks.Journal of cheminformatics · 2026Article
- AI-assisted interpretation of Markush structures in pharmaceutical patents: a review of emerging tools, datasets, and challenges.Journal of cheminformatics · 2026Review
- A review of transformer models in drug discovery and beyond.Journal of pharmaceutical analysis · 2025Review
- Automation and machine learning augmented by large language models in a catalysis study.Chemical science · 2024Review
- ChemReco: automated recognition of hand-drawn carbon-hydrogen-oxygen structures using deep learning.Scientific reports · 2024Article
- Advancements in hand-drawn chemical structure recognition through an enhanced DECIMER architecture.Journal of cheminformatics · 2024Article
- DECIMER.ai: an open platform for automated optical chemical structure identification, segmentation and recognition in scientific publications.Nature communications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Extraction of chemical formulas from images was not in the top priority of Computer Vision tasks for a while. The complexity both on the input and prediction sides has made this task challenging for the conventional Artificial Intelligence and Machine Learning problems. A binary input image which might seem trivial for convolutional analysis was not easy to classify, since the provided sample was not representative of the given molecule: to describe the same formula, a variety of graphical representations which do not resemble each other can be used. Considering the variety of molecules, the problem shifted from classification to that of formula generation, which makes Natural Language Processing (NLP) a good candidate for an effective solution. This paper describes the evolution of approaches from rule-based structure analyses to complex statistical models, and compares the efficiency of models and methodologies used in the recent years. Although the latest achievements deliver ideal results on particular datasets, the authors mention possible problems for various scenarios and provide suggestions for further development.
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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.