药企自研的科研助手，用大模型整合多源数据辅助研发决策。
Research Assistant: AstraZeneca's Agentic System for R&D
- 基于大模型构建多步推理系统，支持问答与复杂研究任务。
- 整合文献、临床试验、化学等多源数据，回答有据可查。
- 适合医药研发人员日常使用，提升跨数据源探索效率。
我们介绍 Research Assistant，这是阿斯利康内部开发的基于大语言模型的系统，旨在帮助科学家和临床医生在广泛的数据库中探索生物医学问题。该系统提供对话式界面，整合科学文献、知识图谱、化学数据、临床试验、安全资源、表达数据及内部实验系统中的证据。它支持快速问答模式和多步骤复杂研究任务模式。所有响应均基于检索到的证据，并链接至原始来源，便于用户审查和深入探索数据。本文简要介绍了系统架构、产品设计核心决策，以及在阿斯利康全公司范围内部署以支持日常研发工作流程的经验教训。
原文摘要 · Abstract (English)
We describe Research Assistant, an internal LLM-based system developed at AstraZeneca to help scientists and clinicians explore biomedical questions across a broad range of data sources. The system provides a chat-style interface that brings together evidence from scientific literature, knowledge graphs, chemistry, clinical trials, safety resources, expression data, and internal experimental systems. It supports both a fast mode for direct question answering and a multi-step mode for more complex research tasks. Responses are grounded in retrieved evidence and linked back to the original sources, allowing users to review and further explore the underlying data. In this technical note, we outline the system architecture, the main design choices behind the product, and lessons learned from deploying it at scale to support day-to-day R&D workflows across AstraZeneca.
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