ArticleJournal of cheminformatics2021
DECIMER 1.0: deep learning for chemical image recognition using transformers.
Article in Journal of cheminformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 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
- Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms.Chemical reviews · 2026Review
- Librarian of Alexandria: A Modular Chemical Data Extraction Pipeline to Compare LLM Performance.Journal of chemical information and modeling · 2026Article
- Efficient fine-tuning of vision-language adapters in chemical VLMs for molecular image-text tasks.Journal of cheminformatics · 2026Article
- AdaptMol: domain adaptation for molecular image recognition with limited supervision.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
- RGReco: a unified framework for automated R-group recognition in chemical publications.Journal of cheminformatics · 2026Article
- ReactionSeek: LLM-powered literature data mining and knowledge discovery in organic synthesis.Nature communications · 2026Article
- A survey of contrastive learning methods in molecular representation.Briefings in bioinformatics · 2026Review
- Subgrapher: visual fingerprinting of chemical structures.Journal of cheminformatics · 2025Article
- A review of transformer models in drug discovery and beyond.Journal of pharmaceutical analysis · 2025Review
- A review of large language models and autonomous agents in chemistry.Chemical science · 2025Review
- MolNexTR: a generalized deep learning model for molecular image recognition.Journal of cheminformatics · 2024Article
- 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
- Application of Transformers in Cheminformatics.Journal of chemical information and modeling · 2024Review
- HBCVTr: an end-to-end transformer with a deep neural network hybrid model for anti-HBV and HCV activity predictor from SMILES.Scientific reports · 2024Article
- pKalculator: A pBeilstein journal of organic chemistry · 2024Article
- PDBe CCDUtils: an RDKit-based toolkit for handling and analysing small molecules in the Protein Data Bank.Journal of cheminformatics · 2023Article
- YoDe-Segmentation: automated noise-free retrieval of molecular structures from scientific publications.Journal of cheminformatics · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The amount of data available on chemical structures and their properties has increased steadily over the past decades. In particular, articles published before the mid-1990 are available only in printed or scanned form. The extraction and storage of data from those articles in a publicly accessible database are desirable, but doing this manually is a slow and error-prone process. In order to extract chemical structure depictions and convert them into a computer-readable format, Optical Chemical Structure Recognition (OCSR) tools were developed where the best performing OCSR tools are mostly rule-based. The DECIMER (Deep lEarning for Chemical ImagE Recognition) project was launched to address the OCSR problem with the latest computational intelligence methods to provide an automated open-source software solution. Various current deep learning approaches were explored to seek a best-fitting solution to the problem. In a preliminary communication, we outlined the prospect of being able to predict SMILES encodings of chemical structure depictions with about 90% accuracy using a dataset of 50-100 million molecules. In this article, the new DECIMER model is presented, a transformer-based network, which can predict SMILES with above 96% accuracy from depictions of chemical structures without stereochemical information and above 89% accuracy for depictions with stereochemical information.
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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.