MOSES introduces a comprehensive benchmarking platform for molecular generative models, offering standardized datasets, evaluation metrics, and baselines. By providing a unified measuring stick, it aims to resolve reproducibility challenges in chemical distribution learning.
PubMed-OCR provides 1.5M pages of scientific articles with comprehensive OCR annotations and bounding boxes to support layout-aware modeling and document analysis.
ChemDFM-R is a 14B-parameter chemical reasoning model that integrates a 101B-token dataset of atomized chemical knowledge. Using a mix-sourced distillation strategy and domain-specific reinforcement learning, it outperforms similarly sized models and DeepSeek-R1 on ChemEval.
This paper introduces ChemBERTa, a RoBERTa-based model pretrained on 77M SMILES strings. It systematically evaluates the impact of pretraining dataset size, tokenization strategies, and input representations (SMILES vs. SELFIES) on downstream MoleculeNet tasks, finding that performance scales positively with data size.
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.
Introduces AtomLenz, an OCSR tool that combines object detection with a molecular graph constructor. Features a novel weakly supervised training scheme (ProbKT*) to learn atom-level localization from SMILES-only data, achieving state-of-the-art results on hand-drawn images.
ChemReco automates the recognition of hand-drawn chemical structures using a synthetic data pipeline and an EfficientNet+Transformer architecture, achieving 96.90% accuracy on C-H-O molecules.
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.
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.
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.
MolMiner replaces traditional rule-based vectorization with a deep learning object detection pipeline (YOLOv5) to extract chemical structures from PDFs. It outperforms open-source baselines on four benchmarks and introduces a new real-world dataset of 3,040 images.
Proposes an end-to-end architecture replacing standard CNN backbones with Swin Transformer to capture global image context. Introduces Multi-label Focal Loss to handle severe token imbalance in chemical datasets.