ChemMiner：一种用于化学文献数据挖掘的大型语言模型智能体系统
ChemMiner: A Large Language Model Agent System for Chemical Literature Data Mining
AI总结：
本文提出ChemMiner，一个利用LLM多智能体从化学文献中提取高保真反应数据的端到端框架，包含文本分析、多模态和合成分析三个专用智能体，并构建专家标注基准，实验表明其识别率与人类化学家相当且显著减少处理时间。
摘要：
AI辅助化学合成工具的开发需要涵盖多种反应类型的综合数据集，然而当前的高通量实验（HTE）方法成本高昂且范围受限。化学文献是一个庞大的、未充分探索的数据源，包含每年发表的数千种反应。然而，从文献中提取反应信息面临重大挑战，包括多样的写作风格、复杂的共指关系以及多模态信息呈现。本文提出ChemMiner，一种新型端到端框架，利用由大型语言模型（LLMs）驱动的多个智能体从文献中提取高保真化学数据。ChemMiner包含三个专用智能体：用于共指映射的文本分析智能体、用于非文本信息提取的多模态智能体，以及用于数据生成的合成分析智能体。此外，我们开发了一个包含专家标注化学文献的综合基准，以评估提取效率和精度。实验结果表明，反应识别率与人类化学家相当，同时显著减少了处理时间，并具有高准确率、召回率和F1分数。我们的开源基准促进了化学文献数据挖掘的未来研究。
英文摘要：
The development of AI-assisted chemical synthesis tools requires comprehensive datasets covering diverse reaction types, yet current high-throughput experimental (HTE) approaches are expensive and limited in scope. Chemical literature represents a vast, underexplored data source containing thousands of reactions published annually. However, extracting reaction information from literature faces significant challenges including varied writing styles, complex coreference relationships, and multimodal information presentation. This paper proposes ChemMiner, a novel end-to-end framework leveraging multiple agents powered by large language models (LLMs) to extract high-fidelity chemical data from literature. 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. Furthermore, we developed a comprehensive benchmark with expert-annotated chemical literature to evaluate both extraction efficiency and precision. Experimental results demonstrate reaction identification rates comparable to human chemists while significantly reducing processing time, with high accuracy, recall, and F1 scores. Our open-sourced benchmark facilitates future research in chemical literature data mining.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
