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

• URL: http://arxiv.org/abs/2609.06703v1
• Date: Sun, 06 Sep 2026 16:20:43 GMT
• Title: DianShi-RxnDB: A Large-Scale, Fine-Grained Organic Reaction Data Platform Built via a Fully Automated Pipeline for Researchers and AI Agents
• Abstract summary: We present DianShi-RxnDB, a large-scale, fine-grained organic reaction data platform.<n>It is built via a fully automated extraction and normalization pipeline.<n>The platform provides a Web research workbench for searching, filtering, comparing, and source-verifying records.
• Score: 77.19284650368924
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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/ .

Related papers

• A Self-Evolving Agentic System for Automated Generation and Execution of Biological Protocols [26.510927882022884]
  ProtoPilot is a self-evolving multi-agent system for wet-lab automation.<n>It incorporates layer-wise verifiability, multi-agent orchestration and a runtime skill-updated library to generate protocols.<n>It achieved a Top@3 expert-preference rate of 90.2%, an overall protocol-tocode gate pass rate of 89.5% and an Opentrons pass rate of 88.24%.
  arXiv  Detail & Related papers  (2026-06-30T14:51:13Z)
• AgentCAT: An LLM Agent for Extracting and Analyzing Catalytic Reaction Data from Chemical Engineering Literature [55.66036140125613]
  This paper presents a large language model (LLM) agent named AgentCAT, which extracts and analyzes catalytic reaction data from chemical engineering papers.<n>AgentCAT serves as an alternative to overcome the long-standing data bottleneck in chemical engineering field.
  arXiv  Detail & Related papers  (2026-02-10T04:30:11Z)
• zERExtractor:An Automated Platform for Enzyme-Catalyzed Reaction Data Extraction from Scientific Literature [12.109637682144125]
  zERExtractor is an automated platform for comprehensive extraction of enzyme-catalyzed reaction and activity data from scientific literature.<n>Our pipeline combines domain-adapted deep learning, advanced OCR, semantic entity recognition, and prompt-driven LLM modules.<n>We release a large benchmark dataset comprising over 1,000 annotated tables and 5,000 biological fields from 270 P450-related enzymology publications.
  arXiv  Detail & Related papers  (2025-07-30T07:21:32Z)
• A Multi-Agent System Enables Versatile Information Extraction from the Chemical Literature [8.306442315850878]
  We develop a multimodal large language model (MLLM)-based multi-agent system for robust and automated chemical information extraction.<n>Our system achieved an F1 score of 80.8% on a benchmark dataset of sophisticated multimodal chemical reaction graphics from the literature.
  arXiv  Detail & Related papers  (2025-07-27T11:16:57Z)
• ChemActor: Enhancing Automated Extraction of Chemical Synthesis Actions with LLM-Generated Data [53.78763789036172]
  We present ChemActor, a fully fine-tuned large language model (LLM) as a chemical executor to convert between unstructured experimental procedures and structured action sequences.<n>This framework integrates a data selection module that selects data based on distribution divergence, with a general-purpose LLM, to generate machine-executable actions from a single molecule input.<n>Experiments on reaction-to-description (R2D) and description-to-action (D2A) tasks demonstrate that ChemActor achieves state-of-the-art performance, outperforming the baseline model by 10%.
  arXiv  Detail & Related papers  (2025-06-30T05:11:19Z)
• Interpretable Deep Learning for Polar Mechanistic Reaction Prediction [43.95903801494905]
  We introduce PMechRP (Polar Mechanistic Reaction Predictor), a system that trains machine learning models on the PMechDB dataset.<n>We train compare a range of machine learning models, including transformer-based, graph-based and two-step siamese architectures.<n>Our best-performing approach was a hybrid model, which combines a 5-ensemble of Chemformer models with a two-step Siamese framework.
  arXiv  Detail & Related papers  (2025-04-22T02:31:23Z)
• BatGPT-Chem: A Foundation Large Model For Retrosynthesis Prediction [65.93303145891628]
  BatGPT-Chem is a large language model with 15 billion parameters, tailored for enhanced retrosynthesis prediction. Our model captures a broad spectrum of chemical knowledge, enabling precise prediction of reaction conditions. This development empowers chemists to adeptly address novel compounds, potentially expediting the innovation cycle in drug manufacturing and materials science.
  arXiv  Detail & Related papers  (2024-08-19T05:17:40Z)
• ChemMiner: A Large Language Model Agent System for Chemical Literature Data Mining [56.15126714863963]
  ChemMiner is an end-to-end framework for extracting chemical data from literature.<n>ChemMiner incorporates three specialized agents: a text analysis agent for coreference mapping, a multimodal agent for non-textual information extraction, and a synthesis analysis agent for data generation.<n> Experimental results demonstrate reaction identification rates comparable to human chemists while significantly reducing processing time, with high accuracy, recall, and F1 scores.
  arXiv  Detail & Related papers  (2024-02-20T13:21:46Z)
• ChemiRise: a data-driven retrosynthesis engine [19.52621175562223]
  ChemiRise can propose complete retrosynthesis routes for organic compounds rapidly and reliably. System was trained on a processed patent database of over 3 million organic reactions.
  arXiv  Detail & Related papers  (2021-08-09T05:13:14Z)
• RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning [107.64562550844146]
  Retrosynthesis is an emerging research area of deep learning. We propose a new approach that reformulating retrosynthesis into a selection problem of reactants from a candidate set of commercially available molecules. For learning the score functions, we also propose a novel contrastive training scheme with hard negative mining.
  arXiv  Detail & Related papers  (2021-05-03T12:47:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.