ArticleJournal of cheminformatics2022
SwinOCSR: end-to-end optical chemical structure recognition using a Swin Transformer.
Article 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 13 papers.
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Who cites it
13 citing papers in PubMed.
- CUE: A Chemical Uncertainty-Aware Embedding Framework for Multimodal Drug Selectivity Prediction.Journal of chemical information and modeling · 2026Article
- A Benchmark Evaluation of Chemical Structure Extraction from Patents: Insights and Challenges in Chemical Structure Recognition.Chemical research in toxicology · 2026Article
- AdaptMol: domain adaptation for molecular image recognition with limited supervision.Journal of cheminformatics · 2026Article
- GraphReco: Probabilistic Structure Recognition for Chemical Molecules.ChemistryOpen · 2026Article
- RGReco: a unified framework for automated R-group recognition in chemical publications.Journal of cheminformatics · 2026Article
- AI-Powered Workflow for Constructing Organic Materials Databases from the Literature: Integrating Large Language Models.ACS omega · 2025Article
- Optimizing Mask R-CNN for enhanced quinoa panicle detection and segmentation in precision agriculture.Frontiers in plant science · 2025Article
- MolNexTR: a generalized deep learning model for molecular image recognition.Journal of cheminformatics · 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
- Automated molecular structure segmentation from documents using ChemSAM.Journal of cheminformatics · 2024Article
- YoDe-Segmentation: automated noise-free retrieval of molecular structures from scientific publications.Journal of cheminformatics · 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
5 authors.
Funding
Abstract
Optical chemical structure recognition from scientific publications is essential for rediscovering a chemical structure. It is an extremely challenging problem, and current rule-based and deep-learning methods cannot achieve satisfactory recognition rates. Herein, we propose SwinOCSR, an end-to-end model based on a Swin Transformer. This model uses the Swin Transformer as the backbone to extract image features and introduces Transformer models to convert chemical information from publications into DeepSMILES. A novel chemical structure dataset was constructed to train and verify our method. Our proposed Swin Transformer-based model was extensively tested against the backbone of existing publicly available deep learning methods. The experimental results show that our model significantly outperforms the compared methods, demonstrating the model's effectiveness. Moreover, we used a focal loss to address the token imbalance problem in the text representation of the chemical structure diagram, and our model achieved an accuracy of 98.58%.
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