将化学文献中的结构图与反应式自动转为机器可读数据，精度超93%。
MinerU.Chem: A High-Precision System for Optical Chemical Structure and Reaction Recognition
- 分五步处理化学图像：识别、提取、解析分子与反应
- 在2392个样本上识别准确率达93.02%，比现有系统高18个百分点
- 适合化学信息提取、药物设计等研究者使用
在有机化学论文和专利中，分子结构、反应式及实验条件常以图像形式呈现，通用文档解析系统难以直接转化为机器可读数据，限制了化学知识库构建和化学AI任务的发展。本文介绍MinerU-Chem，一个集成于MinerU平台的化学文献解析系统。基于通用文档解析流程，新增五项化学专用模块：化学相关性过滤、分子结构检测、分子标识符提取、分子结构识别与反应方案解析。该系统将文档中的化学图像区域转换为分子摘要列表与反应摘要列表。分子结构识别采用CARBON（Complex Atomic Representation and Bonding Object Notation）作为核心表示，既保留原始图像布局，又支持复杂化学语义表达，并可导出MolFile、SMILES等标准格式。在MolRecBench-Wild的可评估子集（N=2,392）上，其分子结构识别模块达到93.02%的SMILES精确匹配率，优于最佳对比系统GPT-5.6-Sol（74.87%），提升18.15个百分点。系统已上线，可通过https://mineru.net/OpenSourceTools/Extractor 获取。
原文摘要 · Abstract (English)
In organic chemistry papers and patents, molecular structures, reaction schemes, and experimental conditions are often presented as molecular structure depictions, reaction diagrams, and complex tables or figures. Such information is difficult for general-purpose document parsing systems to directly convert into machine-readable data. This limits data production for organic chemistry knowledge base construction and for AI for Chemistry tasks such as reaction prediction, retrosynthesis, condition recommendation, molecular property prediction, and drug molecule design. This report introduces MinerU-Chem, a document parsing system for organic chemistry literature integrated into the MinerU online platform. Built on top of MinerU's general document parsing pipeline, MinerU-Chem adds five chemistry-specific modules: chemistry relevance filtering, molecular structure detection, molecule identifier extraction, molecular structure recognition, and reaction scheme parsing. Together, these modules convert organic-chemistry-related image regions in documents into a Molecule Summary List and a Reaction Summary List. For molecular structure recognition, MinerU-Chem uses CARBON (Complex Atomic Representation and Bonding Object Notation) as its core representation. CARBON enables recognition results to preserve both the visual layout of the original image and complex chemical semantics, while supporting the export of standard downstream formats such as MolFile and SMILES. On the SMILES-evaluable subset of MolRecBench-Wild (N=2,392), MinerU-Chem's molecular structure recognition module achieves a SMILES exact-match accuracy of 93.02%, outperforming the best evaluated comparison system, GPT-5.6-Sol (74.87%), by 18.15 percentage points. The system has been integrated into the MinerU online platform and is available at https://mineru.net/OpenSourceTools/Extractor .
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