Molecular-Representation

STOUT V2.0 uses Transformers trained on ~1 billion SMILES-IUPAC pairs to accurately translate chemical structures into systematic names (and vice-versa), outperforming its RNN predecessor.

STOUT (SMILES-TO-IUPAC-name translator) uses neural machine translation to convert chemical line notations to IUPAC names and vice versa, achieving ~90% BLEU score. It addresses the lack of open-source tools for algorithmic IUPAC naming.

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.

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.

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.

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.