This paper introduces Chemformer, a BART-based sequence-to-sequence model pre-trained on 100M molecules using a ‘combined’ masking and augmentation task. It achieves top-1 accuracy on reaction prediction benchmarks while significantly reducing training time through transfer learning.
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.
MERMaid leverages fine-tuned vision models and VLM reasoning to mine chemical reaction data directly from PDF figures and tables. By handling context inference and coreference resolution, it builds high-fidelity knowledge graphs with 87% end-to-end accuracy.
MOFFlow is the first deep generative model tailored for Metal-Organic Framework (MOF) structure prediction. It utilizes Riemannian flow matching on SE(3) to assemble rigid building blocks (metal nodes and organic linkers), achieving higher accuracy and scalability than atom-based methods on large systems.
This paper presents a multimodal search system that facilitates passage-level retrieval of chemical reactions and molecular structures by linking diagrams, text, and reaction records extracted from scientific PDFs.
OCSAug uses Denoising Diffusion Probabilistic Models (DDPM) and the RePaint algorithm with custom masking to generate synthetic hand-drawn chemical structure images, improving OCSR performance by 1.918-3.820x on the DECIMER benchmark.
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.