ChemDFM-X is a multimodal chemical foundation model that integrates five non-text modalities (2D graphs, 3D conformations, images, MS2 spectra, IR spectra) into a single LLM decoder. It overcomes data scarcity by generating a 7.6M instruction-tuning dataset through approximate calculations and model predictions, establishing strong baseline performance across multiple modalities.
Deep dive into 24 image-to-sequence OCSR methods (2019-2025), comparing encoder-decoder architectures, molecular string representations, training scale, and hardware requirements.
InstructMol integrates a pre-trained molecular graph encoder (MoleculeSTM) with a Vicuna-7B LLM using a linear projector. It employs a two-stage training process (alignment pre-training followed by task-specific instruction tuning with LoRA) to excel at property prediction, description generation, and reaction analysis.
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.
DECIMER.ai addresses the lack of open tools for Optical Chemical Structure Recognition (OCSR) by providing a comprehensive, deep-learning-based workflow. It features a novel data generation pipeline (RanDepict), a web application, and models for segmentation and recognition that rival or exceed proprietary solutions.
Proposes a new architecture (DGAT) to resolve global context loss in chemical structure recognition. Introduces Cascaded Global Feature Enhancement and Sparse Differential Global-Local Attention, achieving 84.0% BLEU-4 and handling complex chiral structures implicitly.
This paper presents an enhanced deep learning architecture for Optical Chemical Structure Recognition (OCSR) specifically optimized for hand-drawn inputs. By pairing an EfficientNetV2 encoder with a Transformer decoder and training on over 150 million synthetic images, the model achieves 73.25% exact match accuracy on a real-world hand-drawn benchmark of 5,088 images.
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.