DianShi-RxnDB：一個為研究人員與 AI 代理打造、透過全自動化流程建構的大規模細粒度有機反應資料平台

DianShi-RxnDB: A Large-Scale, Fine-Grained Organic Reaction Data Platform Built via a Fully Automated Pipeline for Researchers and AI Agents

September 6, 2026

作者: Yubin Wang, Xingjian Wei, Jiang Wu, Yinfan Wang, Boyu Zhu, Lin Zhang, Jianing Yu, Huazheng Zeng, Ruiyi Ding, Junyuan Gao, Jiaxing Sun, Lingli Ge, Haote Yang, Jingchao Wang, Aijia Guo, Qian Jiang, Yurui Zhao, Wenjian Zhang, Chen Zhu, Lijun Wu, Xiaolei Yang, Haodong Chen, Junjie Yuan, Zichao Ye, Shaowei Hou, Jing Ye, Jia Yu, Shan Wang, Lijun Wu, Jiantao Qiu, Chao Xu, Yuqiang Li, Guangyu Wang, Bowen Zhou, Dahua Lin, Conghui He

cs.AI

摘要

高品質的結構化有機反應數據對於發展化學人工智慧 (AI4Chem) 至關重要，然而此類知識仍大量散落於專利文本、圖像與反應式中。我們提出 DianShi-RxnDB，一個透過整合專利文本、圖像與反應式的全自動擷取與正規化流程所建構的大規模、細粒度有機反應數據平台。其語料庫涵蓋美國專利商標局 (USPTO) 與歐洲專利局 (EPO) 於 1976 年至 2025 年間公開的有機合成專利，產生約 2400 萬筆反應實例，其中約 1480 萬筆 (61.7%) 通過自動化合格性檢查。每個實例代表一個特定的單步驟實驗，記錄參與物、角色、數量、溫度、反應時間、產率、實驗步驟以及來源專利的溯源連結。在對 1,300 個抽樣合格實例的人工評估中，微平均欄位級準確率為 92.95%。與 Pistachio 的匹配比較進一步顯示，在去重記錄數量、表徵粒度以及欄位級精確一致性方面具有優勢。該平台提供了一個 Web 研究工作台，用於搜尋、篩選、比較和來源驗證記錄，以及一個模型上下文協議 (MCP) 服務，為 AI 代理提供可組合的結構化檢索工具。DianShi-RxnDB 可在 https://dianshi.opendatalab.org.cn/ 獲取。

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/ .