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DianShi-RxnDB: A Large-Scale, Fine-Grained Organic Reaction Data Platform Built via a Fully Automated Pipeline for Researchers and AI Agents — arXiv:2609.06703

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.06703
License (as archived): CC0-1.0
Archived: 2026-10-16T12:45:19.083Z
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How to cite

DianShi-RxnDB: A Large-Scale, Fine-Grained Organic Reaction Data Platform Built via a Fully Automated Pipeline for Researchers and AI Agents — arXiv:2609.06703 — arXiv (en), https://arxiv.org/abs/2609.06703 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_f0d4eec3a2bd (Metraipolis archive dc_f0d4eec3a2bd, sha256 7c832f1de504fdc23d3c62463e0be772c8f33eb77bcb791319d1e211f5e3c537)

BibTeX

@misc{dcf0d4eec3a2bd,
  title = {DianShi-RxnDB: A Large-Scale, Fine-Grained Organic Reaction Data Platform Built via a Fully Automated Pipeline for Researchers and AI Agents — arXiv:2609.06703},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.06703},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_f0d4eec3a2bd; sha256 7c832f1de504fdc23d3c62463e0be772c8f33eb77bcb791319d1e211f5e3c537},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.06703. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 .

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-09 in cs.CL).
Categorie arXiv: Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2609.06703 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T12:30:25.059Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-09-09?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).