Knowledge database development by large language models for countermeasures against viruses and marine toxins

arXiv cs.AI / 4/1/2026

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Key Points

The paper addresses the lack of comprehensive, up-to-date databases for medical countermeasures against major viruses and marine toxins, which can slow evidence-based R&D decisions.
Using two LLMs (ChatGPT and Grok) plus human-provided high-level inputs, the authors identify public sources, collect relevant literature data, and iteratively cross-validate information to build curated, interactive knowledge databases.
For the countermeasure ranking task, the work uses an agentic workflow built from two AI agents—one focused on research and another on decision-making—to rank therapies listed in the database.
The resulting interactive web pages are intended to make access to curated information easier and to support faster, more scalable countermeasure selection and updating.

Abstract

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.