This paper proposes a Transformer-based approach (Struct2IUPAC) to convert chemical structures to IUPAC names, challenging the dominance of rule-based systems. Trained on ~47M PubChem examples, it achieves near-perfect accuracy using a round-trip verification step with OPSIN.
This study presents a sequence-to-sequence Transformer model that translates InChI identifiers into IUPAC names character-by-character. Trained on 10 million PubChem pairs, it achieves 91% accuracy on organic compounds, performing comparably to commercial software.
Introduces AtomLenz, an OCSR tool that combines object detection with a molecular graph constructor. Features a novel weakly supervised training scheme (ProbKT*) to learn atom-level localization from SMILES-only data, achieving state-of-the-art results on hand-drawn images.
Comprehensive evaluation of 8 optical chemical structure recognition tools using a newly curated dataset of 2,702 patent images. Proposes ChemIC, a ResNet-50 classifier to route images to specialized tools based on content type, demonstrating that no single tool excels at all tasks.
ChemReco automates the recognition of hand-drawn chemical structures using a synthetic data pipeline and an EfficientNet+Transformer architecture, achieving 96.90% accuracy on C-H-O molecules.
A 2025 AAAI paper introducing ChemVLM, a domain-specific multimodal LLM (26B parameters). It achieves state-of-the-art performance on chemical OCR, reasoning benchmarks, and molecular understanding tasks by combining vision and language models trained on curated chemistry data.
DECIMER.ai addresses the lack of open tools for Optical Chemical Structure Recognition (OCSR) by providing a comprehensive, deep-learning-based workflow. It features a novel data generation pipeline (RanDepict), a web application, and models for segmentation and recognition that rival or exceed proprietary solutions.
Proposes a new architecture (DGAT) to resolve global context loss in chemical structure recognition. Introduces Cascaded Global Feature Enhancement and Sparse Differential Global-Local Attention, achieving 84.0% BLEU-4 and handling complex chiral structures implicitly.
This paper presents an enhanced deep learning architecture for Optical Chemical Structure Recognition (OCSR) specifically optimized for hand-drawn inputs. By pairing an EfficientNetV2 encoder with a Transformer decoder and training on over 150 million synthetic images, the model achieves 73.25% exact match accuracy on a real-world hand-drawn benchmark of 5,088 images.
Proposes Image2InChI, an OCSR model with improved SwinTransformer encoder and novel feature fusion network with attention mechanisms that achieves 99.8% InChI accuracy on the BMS dataset.
This paper introduces a multi-modal approach for extracting chemical Markush structures from patents, combining a Vision-Text-Layout encoder with a specialized chemical vision encoder. It addresses the lack of training data with a synthetic generation pipeline and introduces M2S, a new real-world benchmark.
MMSSC-Net introduces a multi-stage cognitive approach for OCSR, utilizing a SwinV2 encoder and GPT-2 decoder to recognize atomic and bond sequences. It achieves 75-98% accuracy across benchmark datasets by handling varying image resolutions and noise through fine-grained perception of atoms and bonds.