ArticleJournal of cheminformatics2020
DECIMER: towards deep learning for chemical image recognition.
Article in Journal of cheminformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- AdaptMol: domain adaptation for molecular image recognition with limited supervision.Journal of cheminformatics · 2026Article
- GraphReco: Probabilistic Structure Recognition for Chemical Molecules.ChemistryOpen · 2026Article
- AntibioticDB: An Updated and Improved Open-Access Database for the Antibacterial Research and Development Community.ACS infectious diseases · 2026Article
- Federated high order tensor fusion for privacy preserving multimodal social media analysis.PloS one · 2026Article
- AI-Powered Workflow for Constructing Organic Materials Databases from the Literature: Integrating Large Language Models.ACS omega · 2025Article
- Inhibition of Casein Kinase 1International journal of molecular sciences · 2025Article
- Deep learning in renal ultrasound: applications, challenges, and future outlook.Frontiers in oncology · 2025Review
- Role of Artificial Intelligence in Drug Discovery to Revolutionize the Pharmaceutical Industry: Resources, Methods and Applications.Recent patents on biotechnology · 2025Review
- STOUT V2.0: SMILES to IUPAC name conversion using transformer models.Journal of cheminformatics · 2024Article
- MolNexTR: a generalized deep learning model for molecular image recognition.Journal of cheminformatics · 2024Article
- Revealing Chemical Trends: Insights from Data-Driven Visualization and Patent Analysis in Exposomics Research.Environmental science & technology letters · 2024Article
- 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
- Representations of lipid nanoparticles using large language models for transfection efficiency prediction.Bioinformatics (Oxford, England) · 2024Article
- Small molecule autoencoders: architecture engineering to optimize latent space utility and sustainability.Journal of cheminformatics · 2024Article
- pKalculator: A pBeilstein journal of organic chemistry · 2024Article
- YoDe-Segmentation: automated noise-free retrieval of molecular structures from scientific publications.Journal of cheminformatics · 2023Article
- ReactionDataExtractor 2.0: A Deep Learning Approach for Data Extraction from Chemical Reaction Schemes.Journal of chemical information and modeling · 2023Article
- DECIMER.ai: an open platform for automated optical chemical structure identification, segmentation and recognition in scientific publications.Nature communications · 2023Article
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Authors and funding
3 authors.
Funding
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Abstract
The automatic recognition of chemical structure diagrams from the literature is an indispensable component of workflows to re-discover information about chemicals and to make it available in open-access databases. Here we report preliminary findings in our development of Deep lEarning for Chemical ImagE Recognition (DECIMER), a deep learning method based on existing show-and-tell deep neural networks, which makes very few assumptions about the structure of the underlying problem. It translates a bitmap image of a molecule, as found in publications, into a SMILES. The training state reported here does not yet rival the performance of existing traditional approaches, but we present evidence that our method will reach a comparable detection power with sufficient training time. Training success of DECIMER depends on the input data representation: DeepSMILES are superior over SMILES and we have a preliminary indication that the recently reported SELFIES outperform DeepSMILES. An extrapolation of our results towards larger training data sizes suggests that we might be able to achieve near-accurate prediction with 50 to 100 million training structures. This work is entirely based on open-source software and open data and is available to the general public for any purpose.
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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.