自动化构建2400万条精细有机反应数据，助力化学AI研究。
DianShi-RxnDB: A Large-Scale, Fine-Grained Organic Reaction Data Platform Built via a Fully Automated Pipeline for Researchers and AI Agents
- 全自动化提取专利文本、图像与反应式中的反应信息
- 覆盖1976-2025年超2400万条反应实例，61.7%通过质量验证
- 支持研究人员与AI代理检索、比对，适合化学生成与逆合成任务
高质量结构化有机反应数据对发展人工智能辅助化学（AI4Chem）至关重要，但大量知识分散于专利文本、图像和反应图示中。我们提出DianShi-RxnDB，一个基于全自动抽取与标准化流程构建的大规模、细粒度有机反应数据平台，涵盖美国专利商标局（USPTO）与欧洲专利局（EPO）1976至2025年间发布的有机合成专利，共生成约2400万条反应实例，其中约1480万条（61.7%）通过自动质量检测。每条记录包含具体单步实验的反应物、角色、用量、温度、时间、产率、操作步骤及来源专利链接。对1300条抽样合格实例的人工评估显示，字段级微平均准确率达92.95%。与Pistachio的对比表明，在去重记录数、表示粒度和字段级完全一致方面更具优势。平台提供网页研究工作台用于搜索、筛选、比对与溯源验证，并开放模型上下文协议（MCP）服务，供AI代理调用结构化检索工具。DianShi-RxnDB可在https://dianshi.opendatalab.org.cn/ 获取。
原文摘要 · Abstract (English)
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/ .
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