TeroSeek用AI整合萜类化合物研究，让跨学科查询更精准
TeroSeek: An AI-Powered Knowledge Base and Retrieval Generation Platform for Terpenoid Research
- 基于二十年文献构建专业知识库，结合检索增强生成技术
- 在萜类相关问题上优于通用大模型，信息准确率更高
- 适合药理、化学、生物等领域的研究人员快速获取知识
萜类化合物是研究超过150年的关键天然产物，因其横跨化学、药理学和生物学的多学科特性，知识整合面临挑战。为此，作者开发了TeroSeek，一个从二十年萜类文献中构建的精选知识库，结合AI问答聊天机器人与网络服务。利用检索增强生成（RAG）框架，TeroSeek提供结构化、高质量的信息，在萜类相关查询中表现优于通用大语言模型（LLMs）。该平台作为领域专用专家工具，支持多学科研究，现已公开访问：http://teroseek.qmclab.com。
原文摘要 · Abstract (English)
Terpenoids are a crucial class of natural products that have been studied for over 150 years, but their interdisciplinary nature (spanning chemistry, pharmacology, and biology) complicates knowledge integration. To address this, the authors developed TeroSeek, a curated knowledge base (KB) built from two decades of terpenoid literature, coupled with an AI-powered question-answering chatbot and web service. Leveraging a retrieval-augmented generation (RAG) framework, TeroSeek provides structured, high-quality information and outperforms general-purpose large language models (LLMs) in terpenoid-related queries. It serves as a domain-specific expert tool for multidisciplinary research and is publicly available at http://teroseek.qmclab.com.
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