用大模型构建病毒与海洋毒素防治数据库，助力快速决策。
Knowledge database development by large language models for countermeasures against viruses and marine toxins
- 用ChatGPT和Grok构建五个病毒与海洋毒素的综合数据库。
- 通过迭代交叉验证，实现信息精准采集与整理。
- 支持交互式网页访问，适合科研人员快速获取证据。
获取病毒与海洋毒素最新防治措施信息对研发有效治疗手段至关重要，但现有数据库普遍缺乏系统性，导致决策效率低下。本文利用ChatGPT与Grok两款大语言模型，针对拉萨、马尔堡、埃博拉、尼帕及委内瑞拉马脑炎五种病毒以及海洋毒素，构建了综合性治疗对策数据库。在人工输入指导下，两模型识别公开数据库与文献，收集相关数据，通过迭代交叉验证确保准确性，并设计交互式网页以方便访问。特别地，使用ChatGPT构建智能代理工作流（含研究与决策两个代理），对各类对策进行排序。本研究探索了大语言模型作为可扩展、可更新的知识库建设方案，在支持循证决策方面具有潜力。
原文摘要 · Abstract (English)
Access to the most up-to-date information on medical countermeasures is important for the research and development of effective treatments for viruses and marine toxins. However, there is a lack of comprehensive databases that curate data on viruses and marine toxins, making decisions on medical countermeasures slow and difficult. In this work, we employ two large language models (LLMs) of ChatGPT and Grok to design two comprehensive databases of therapeutic countermeasures for five viruses of Lassa, Marburg, Ebola, Nipah, and Venezuelan equine encephalitis, as well as marine toxins. With high-level human-provided inputs, the two LLMs identify public databases containing data on the five viruses and marine toxins, collect relevant information from these databases and the literature, iteratively cross-validate the collected information, and design interactive webpages for easy access to the curated, comprehensive databases. Notably, the ChatGPT LLM is employed to design agentic AI workflows (consisting of two AI agents for research and decision-making) to rank countermeasures for viruses and marine toxins in the databases. Together, our work explores the potential of LLMs as a scalable, updatable approach for building comprehensive knowledge databases and supporting evidence-based decision-making.
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