PAPER / ARXIV:2609.06703
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
RESUMO
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 this https URL .
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