TourSynbio-Search：一种用于蛋白质工程的统一搜索方法的大语言模型驱动智能体框架
TourSynbio-Search: A Large Language Model Driven Agent Framework for Unified Search Method for Protein Engineering
AI总结：
TourSynbio-Search是TourSynbio-7B驱动的双模块搜索智能体框架，可跨多平台检索文献与蛋白质数据，降低技术门槛，提升蛋白质工程研究的信息检索效率与生产力
摘要：
蛋白质相关数据库与科学文献呈指数级增长，加之对高效生物信息检索的需求日益提升，蛋白质工程研究亟需统一且易访问的搜索方法。本文提出TourSynbio-Search，这是一种由蛋白质多模态大语言模型（LLM）TourSynbio-7B驱动的新型生物信息学搜索智能体框架，旨在应对快速扩张的蛋白质数据库及对应在线研究文献带来的信息检索挑战。该智能体采用双模块架构，包含PaperSearch与ProteinSearch组件，可全面探索多个生物数据库中的科学文献与蛋白质数据。其核心是智能体系统，能解析自然语言查询、优化搜索参数，并在UniProt、PDB、ArXiv和BioRxiv等主要平台执行搜索操作。该智能体处理直观自然语言查询的能力降低了技术门槛，使研究人员无需具备广泛的生物信息学专业知识，即可高效访问和分析复杂生物数据。通过文献检索与蛋白质结构可视化的详细案例研究，我们证明TourSynbio-Search在简化生物信息检索、提升研究生产力方面的有效性。该框架缩小了复杂生物数据库与研究人员之间的可及性差距，有望加速蛋白质工程应用的进展。我们的代码可在以下网址获取：this https URL
英文摘要：
The exponential growth in protein-related databases and scientific literature, combined with increasing demands for efficient biological information retrieval, has created an urgent need for unified and accessible search methods in protein engineering research. We present TourSynbio-Search, a novel bioinformatics search agent framework powered by the TourSynbio-7B protein multimodal large language model (LLM), designed to address the growing challenges of information retrieval across rapidly expanding protein databases and corresponding online research literature. The agent's dual-module architecture consists of PaperSearch and ProteinSearch components, enabling comprehensive exploration of both scientific literature and protein data across multiple biological databases. At its core, TourSynbio-Search employs an intelligent agent system that interprets natural language queries, optimizes search parameters, and executes search operations across major platforms including UniProt, PDB, ArXiv, and BioRxiv. The agent's ability to process intuitive natural language queries reduces technical barriers, allowing researchers to efficiently access and analyze complex biological data without requiring extensive bioinformatics expertise. Through detailed case studies in literature retrieval and protein structure visualization, we demonstrate TourSynbio-Search's effectiveness in streamlining biological information retrieval and enhancing research productivity. This framework represents an advancement in bridging the accessibility gap between complex biological databases and researchers, potentially accelerating progress in protein engineering applications. Our codes are available at: https://github.com/tsynbio/Toursynbio-Search
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。
