MolSight introduces a three-stage training paradigm for Optical Chemical Structure Recognition (OCSR), utilizing large-scale pretraining, multi-granularity fine-tuning with auxiliary bond and coordinate prediction tasks, and reinforcement learning (GRPO) to achieve state-of-the-art performance in recognizing complex stereochemical structures like chiral centers and cis-trans isomers.
Proposes the ‘Optical Chemical Structure Understanding’ (OCSU) task to translate molecular images into multi-level descriptions (motifs, IUPAC, SMILES). Introduces the Vis-CheBI20 dataset and two paradigms: DoubleCheck (OCSR-based) and Mol-VL (OCSR-free).
DECIMER 1.0 introduces a Transformer-based architecture coupled with EfficientNet-B3 to solve Optical Chemical Structure Recognition. By leveraging the robust SELFIES representation and scaling training to over 35 million molecules, it achieves state-of-the-art accuracy on synthetic benchmarks, offering an open-source solution for mining chemical data from legacy literature.
This paper introduces a convolution-free, end-to-end transformer model for molecular image translation. By replacing CNN encoders with Vision Transformers, it achieves superior performance on noisy datasets compared to ResNet-LSTM baselines.
This paper introduces ICMDT, a Transformer-based architecture for molecular translation (image-to-InChI). By enhancing the TNT block to fuse pixel, small patch, and large patch embeddings, the model achieves superior accuracy on the Bristol-Myers Squibb dataset compared to CNN-RNN and standard Transformer baselines.
A Transformer-based system for optical chemical structure recognition introducing a comprehensive data generation pipeline (FG-SMILES, Markush structures, visual contamination) achieving 79% accuracy on real-world images, outperforming rule-based systems like OSRA.
This methodological study isolates the impact of chemical string representations on image-to-text translation models. It finds that while SMILES offers the highest overall accuracy, SELFIES provides a guarantee of structural validity, offering a trade-off for OCSR tasks.
Proposes an end-to-end architecture replacing standard CNN backbones with Swin Transformer to capture global image context. Introduces Multi-label Focal Loss to handle severe token imbalance in chemical datasets.
A 2021 image-to-text approach treating OCSR as an image captioning task. It uses Transformers with SELFIES representation to convert molecular structure diagrams into SMILES strings, unlocking visual chemical knowledge from scientific literature.
A 2025 Vision-Language Model for OCSR that uses graph traversal chain-of-thought reasoning to handle chemical abbreviations (Ph, Et, etc.) that break existing systems, achieving state-of-the-art performance by training on data where abbreviations are preserved.
A 2024 deep learning system for optical chemical structure recognition designed specifically for biomedical literature mining, using ResNet-Transformer architecture to handle challenging conditions including low-resolution images, noise, distortions, and even hand-drawn molecular diagrams from scientific documents.
The MolParser project introduces two key datasets: MolParser-7M, the largest training dataset for Optical Chemical Structure Recognition (OCSR) with 7.7M pairs of images and E-SMILES strings, and WildMol, a new 20k-sample benchmark for evaluating models on challenging real-world data. The training data uniquely combines millions of diverse synthetic molecules with 400,000 manually annotated in-the-wild samples to significantly enhance model robustness.