Ensemble-Conditioned Molecular Design
该研究通过引入集合条件引导框架，优化分子构象集合的模式和属性，解决分子设计中考虑单一生物活性构象的问题。
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LLM Agents Perform Controlled Experiments Using Simulation Models
本文提出一个多代理框架，使大型语言模型能够通过科学模拟模型进行受控实验，以优化制药过程设计，提高输出的具体性和实用性。
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Calibration of clinical trial sample size based on design utility
论文提出了一种设计效用指数，用于临床试验样本量的校准，平衡了高样本量带来的功率增加与最小可检测效应值减小之间的关系。
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Designing a Robust LLM-Based Evaluation System for Agentic AI in Drug Discovery Through Human Alignment
为解决药物发现中AI助手输出评估难题，本文提出基于LLM的评价框架，通过定义评估维度、验证与人类专家的一致性及优化LLM裁判来提高评估准确性。
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Research Assistant: AstraZeneca's Agentic System for R&D
This work addresses the challenge of efficiently integrating heterogeneous, multi-source data in biomedical research by proposing and implementing an intelligent scientific assistant powered by large language models. The system unifies multimodal data—including scientific literature, knowledge graphs, chemical databases, and clinical trial records—through semantic retrieval, enabling both question-answering and multi-step reasoning interactions. It incorporates an evidence-tracing mechanism to ensure interpretability and auditability of its outputs. As the first system to achieve cross-source semantic integration and traceable reasoning in pharmaceutical R&D, it has been deployed across AstraZeneca’s global research infrastructure, significantly enhancing researchers’ information retrieval efficiency and their capacity for automated exploration of complex drug discovery tasks.
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Ensemble-Conditioned Molecular Design
该研究通过引入集合条件引导框架，优化分子构象集合的模式和属性，解决分子设计中考虑单一生物活性构象的问题。
0 citationsRead paper
LLM Agents Perform Controlled Experiments Using Simulation Models
本文提出一个多代理框架，使大型语言模型能够通过科学模拟模型进行受控实验，以优化制药过程设计，提高输出的具体性和实用性。
0 citationsRead paper
Calibration of clinical trial sample size based on design utility
论文提出了一种设计效用指数，用于临床试验样本量的校准，平衡了高样本量带来的功率增加与最小可检测效应值减小之间的关系。
0 citationsRead paper
Designing a Robust LLM-Based Evaluation System for Agentic AI in Drug Discovery Through Human Alignment
为解决药物发现中AI助手输出评估难题，本文提出基于LLM的评价框架，通过定义评估维度、验证与人类专家的一致性及优化LLM裁判来提高评估准确性。
0 citationsRead paper
Research Assistant: AstraZeneca's Agentic System for R&D
This work addresses the challenge of efficiently integrating heterogeneous, multi-source data in biomedical research by proposing and implementing an intelligent scientific assistant powered by large language models. The system unifies multimodal data—including scientific literature, knowledge graphs, chemical databases, and clinical trial records—through semantic retrieval, enabling both question-answering and multi-step reasoning interactions. It incorporates an evidence-tracing mechanism to ensure interpretability and auditability of its outputs. As the first system to achieve cross-source semantic integration and traceable reasoning in pharmaceutical R&D, it has been deployed across AstraZeneca’s global research infrastructure, significantly enhancing researchers’ information retrieval efficiency and their capacity for automated exploration of complex drug discovery tasks.
0 citationsRead paper