Transformers

Transformer-based approach for Optical Chemical Structure Recognition converting chemical images to SELFIES strings with …

Vision Transformer encoder with Transformer decoder for molecular image-to-InChI translation, achieving state-of-the-art …

A Transformer-based model (ICMDT) for converting chemical structure images into InChI text strings using a novel Deep …

Transformer-based OCSR using a novel synthetic data generation pipeline for robust molecular image interpretation across …

Ablation study comparing SMILES, DeepSMILES, SELFIES, and InChI for OCSR. SMILES achieves highest accuracy; SELFIES …

Deep learning model using Swin Transformer and Focal Loss for OCSR, achieving 98.58% accuracy on synthetic benchmarks.

Campos & Ji's method for converting 2D molecular images to SMILES strings using Transformers and SELFIES representation.

GTR-CoT uses graph traversal chain-of-thought reasoning to improve optical chemical structure recognition accuracy.

αExtractor uses ResNet-Transformer to extract chemical structures from literature images, including noisy and hand-drawn …

MolParser-7M is the largest OCSR dataset with 7.7M image-text pairs of molecules and E-SMILES, including 400k real-world …

MolParser converts molecular images from scientific documents to machine-readable formats using end-to-end learning with …

Lu et al. introduce SpaceFormer, a Transformer that models entire 3D molecular space (not just atoms) for superior …