发表机构
Shanghai Artificial Intelligence Laboratory; Fudan University; Shanghai Jiao Tong University; East China Normal University; East China University of Science and Technology（上海人工智能实验室; 复旦大学; 上海交通大学; 华东师范大学; 华东理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出DianShi-RxnDB，一个通过全自动化流水线从USPTO和EPO专利中提取构建的大规模细粒度有机反应数据平台，包含约2400万反应实例，字段级准确率92.95%，并提供Web工作台和MCP服务供研究人员和AI智能体使用。
AI 中文摘要
高质量的有机反应结构化数据对于发展化学人工智能（AI4Chem）至关重要，然而这些知识大多分散在专利文本、图像和反应方案中。我们提出了DianShi-RxnDB，一个大规模、细粒度的有机反应数据平台，通过全自动化的提取和标准化流水线整合专利文本、图像和反应方案。其语料库涵盖1976年至2025年间美国专利商标局（USPTO）和欧洲专利局（EPO）发布的有机合成专利，产生了约2400万个反应实例，其中约1480万（61.7%）通过自动化资格检查。每个实例代表一个具体的单步实验，记录参与者、角色、数量、温度、反应时间、产率、实验步骤以及指向源专利的溯源链接。在1300个抽样合格实例的人工评估中，微平均字段级准确率为92.95%。与Pistachio的匹配比较进一步表明在去重记录数量、表示粒度和字段级精确一致性方面具有优势。该平台提供了一个网络研究工作台，用于搜索、筛选、比较和源验证记录，以及一个模型上下文协议（MCP）服务，为AI智能体提供可组合的结构化检索工具。DianShi-RxnDB可通过此https URL访问。
英文摘要
High-quality structured organic reaction data are essential for developing artificial intelligence for chemistry (AI4Chem), yet much of this knowledge remains dispersed across patent text, images, and reaction schemes. We present DianShi-RxnDB, a large-scale, fine-grained organic reaction data platform built via a fully automated extraction and normalization pipeline integrating patent text, images, and reaction schemes. Its corpus covers organic synthesis patents from the USPTO and EPO published between 1976 and 2025, yielding approximately 24 million reaction instances, of which approximately 14.8 million (61.7%) pass automated qualification checks. Each instance represents a specific single-step experiment recording participants, roles, quantities, temperatures, reaction times, yields, experimental procedures, and provenance links to source patents. In a manual evaluation of 1,300 sampled qualified instances, the micro-averaged field-level accuracy was 92.95%. A matched comparison with Pistachio further indicated advantages in deduplicated record counts, representation granularity, and field-level exact agreement. The platform provides a Web research workbench for searching, filtering, comparing, and source-verifying records, and a Model Context Protocol (MCP) service offering AI agents composable structured retrieval tools. DianShi-RxnDB is available at https://dianshi.opendatalab.org.cn/ .
