GraPPI：用于大规模蛋白质-蛋白质相互作用探索的检索-分解-求解 GraphRAG 框架
GraPPI: A Retrieve-Divide-Solve GraphRAG Framework for Large-scale Protein-protein Interaction Exploration
AI总结：
针对药物发现中靶点识别的语义歧义、缺乏可解释性和检索单元短等局限，提出基于知识图谱的检索-分解-求解智能体框架 GraPPI，通过将 PPI 通路分析分解为子任务，支持大规模蛋白质相互作用信号通路探索。
摘要：
药物发现（DD）对维护和改善公共健康做出了巨大贡献。基于抑制蛋白质错误折叠可减缓疾病进展的假设，研究人员专注于靶点识别（Target ID），以寻找用于药物结合的蛋白质结构。尽管大型语言模型（LLMs）和检索增强生成（RAG）框架加速了药物发现，但将模型整合为连贯的工作流仍然具有挑战性。我们与药物发现研究人员进行了一项用户研究，以确定 LLMs 和 RAGs 在 Target ID 中的适用性。我们确定了两个主要发现：1）LLM 应基于初始蛋白质和具有治疗作用的蛋白质候选物提供多种蛋白质-蛋白质相互作用（PPIs）；2）模型必须提供 PPI 和相关解释以便更好地理解。基于这些观察，我们发现了以往 Target ID 方法中的三个局限性：1）语义歧义，2）缺乏可解释性，以及 3）检索单元短。为了解决这些问题，我们提出了 GraPPI，这是一个基于大规模知识图谱（KG）的检索-分解-求解智能体流水线 RAG 框架，通过将整个 PPI 通路的分析分解为专注于 PPI 边分析的子任务，来支持大规模 PPI 信号通路探索，以理解治疗作用。
英文摘要：
Drug discovery (DD) has tremendously contributed to maintaining and improving public health. Hypothesizing that inhibiting protein misfolding can slow disease progression, researchers focus on target identification (Target ID) to find protein structures for drug binding. While Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) frameworks have accelerated drug discovery, integrating models into cohesive workflows remains challenging. We conducted a user study with drug discovery researchers to identify the applicability of LLMs and RAGs in Target ID. We identified two main findings: 1) an LLM should provide multiple Protein-Protein Interactions (PPIs) based on an initial protein and protein candidates that have a therapeutic impact; 2) the model must provide the PPI and relevant explanations for better understanding. Based on these observations, we identified three limitations in previous approaches for Target ID: 1) semantic ambiguity, 2) lack of explainability, and 3) short retrieval units. To address these issues, we propose GraPPI, a large-scale knowledge graph (KG)-based retrieve-divide-solve agent pipeline RAG framework to support large-scale PPI signaling pathway exploration in understanding therapeutic impacts by decomposing the analysis of entire PPI pathways into sub-tasks focused on the analysis of PPI edges.
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。
