AlgicideDB:一个全面的数据库,增强了用于杀虫剂管理和发现的大型语言模型
Zhangqi Zuo1,2, Jing Hu3, Chaowei Zhang2
1Ocean College, Zhejiang University, Zhoushan, Zhejiang, China.
Frontiers in microbiology
|July 3, 2025
概括
在全球范围内,有害藻类繁殖 (HAB) 正在增加. AlgicideDB是一个新的数据库和人工智能工具,有助于找到更安全,更有效的杀菌剂来控制HAB.
科学领域:
- 环境科学 环境科学
- 生态毒理学 生态毒理学
- 计算生物学 计算生物学
背景情况:
- 有害藻类繁殖 (HAB) 对水生生态系统和人类健康构成日益严重的全球威胁.
- 化学杀藻剂提供快速控制,但由于数据稀缺和环境毒性问题而面临限制.
研究的目的:
- 开发AlgicideDB,一个全面的杀藻剂和它们的特性数据库.
- 为探索杀藻化合物和评估其生态风险创建工具.
- 整合一个由人工智能驱动的系统,用于与HAB相关的查询解决和知识传播.
主要方法:
- 手动整理了1672个杀藻剂记录,包括542种杀藻剂,针对110种藻类.
- 对杀虫剂的物理化学性质的分析和开发一种杀虫剂相似度评分函数.
- 对藻类杀虫剂对非标水生生物的急性毒性的评估.
- 实现一个大型语言模型 (LLM) 与检索增强生成 (RAG) 对于HAB查询.
主要成果:
- AlgicideDB提供了杀虫剂数据的系统资源,包括物理化学特性和毒性信息.
- 一个新的评分功能有助于识别潜在的抗藻类化合物.
- 对各种藻类杀虫剂的生态风险评估数据可用.
- 集成的LLM-RAG系统为HAB信息检索和决策支持提供了一个先进的工具.
结论:
- AlgicideDB是一个有价值的,创新的平台,用于探索杀菌化合物.
- 该数据库有助于制定更安全,更有效的策略来管理有害藻类繁殖.
- 在HAB控制中,AlgicideDB支持知情决策和知识传播.
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