MolGrapher introduces a three-stage pipeline (keypoint detection, supergraph construction, GNN classification) for recognizing chemical structures from images. It achieves 91.5% accuracy on USPTO by treating molecules as graphs, and introduces the USPTO-30K benchmark.
MolMole unifies molecule detection, reaction parsing, and structure recognition into a single vision-based pipeline, achieving top performance on a newly introduced 550-page benchmark by processing full documents without external layout parsers.
MolScribe reformulates molecular recognition as an image-to-graph generation task, explicitly predicting atom coordinates and bonds to better handle stereochemistry and abbreviated structures compared to image-to-SMILES baselines.
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 85.1% stereochemical accuracy on USPTO, recognizing complex stereochemical structures like chiral centers and cis-trans isomers.
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 synthetic data generation pipeline with augmentation, degradation, and background addition to train models that generalize to hand-drawn inputs without seeing real data during training.
DECIMER 1.0 introduces a Transformer-based architecture coupled with EfficientNet-B3 to solve Optical Chemical Structure Recognition. By using the SELFIES representation (which guarantees 100% valid output strings) and scaling training to over 35 million molecules, it achieves 96.47% exact match 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 a Levenshtein distance of 6.95 on noisy datasets, compared to 7.49 for ResNet50-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.