Proposes Ring-Free Language (RFL) to hierarchically decouple molecular graphs into skeletons, rings, and branches, solving issues with 1D serialization of complex 2D structures. Introduces the Molecular Skeleton Decoder (MSD) to progressively predict these components, achieving state-of-the-art results on handwritten and printed chemical structures.
ABC-Net reformulates molecular image recognition as a keypoint detection problem. By predicting atom/bond centers and properties via a single Fully Convolutional Network, it achieves >94% accuracy with high data efficiency.
Proposes a CNN-LSTM architecture that treats chemical structure recognition as an image captioning task. Introduces a robust synthetic data generation pipeline with augmentation, degradation, and background addition to train models that generalize to hand-drawn inputs without seeing real data during pre-training.
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.
Proposes a Random Conditional Guided Decoder (RCGD) and a Structure-Specific Markup Language (SSML) to handle the ambiguity and complexity of handwritten chemical structure recognition, validated on a new benchmark dataset (EDU-CHEMC) with 50,000 handwritten images.
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.
This paper proposes an end-to-end deep learning architecture that translates chemical images directly into molecular graphs using a ResNet-Transformer encoder and a graph-aware decoder. It addresses the limitations of SMILES-based approaches by effectively handling non-atomic symbols (abbreviations) and varying drawing styles found in scientific literature.
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.
MICER treats optical chemical structure recognition as an image captioning task, leveraging transfer learning with a fine-tuned ResNet encoder and attention-based LSTM decoder to convert molecular images into SMILES strings, significantly outperforming rule-based and previous deep learning methods.
MolMiner replaces traditional rule-based vectorization with a deep learning object detection pipeline (YOLOv5) to extract chemical structures from PDFs. It achieves state-of-the-art performance on benchmarks and introduces a new real-world dataset of 3,040 images.
Proposes a patch-based image processing pipeline using Inception V3 to filter Markush structures from chemical documents, significantly outperforming traditional fixed-feature (ORB) methods on low-SNR images.