LLM助力建模疾病机制与药物研发，加速新药上市进程
Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trials
- 用大模型挖掘疾病与靶点的关联，解析复杂生物数据
- 提升药物分子设计效率，预测疗效与安全性
- 适合计算生物学、药物研发及AI+生命科学从业者
大型语言模型（LLMs）在药物发现与开发领域的应用标志着范式转变，为理解疾病机制、推动药物发现及优化临床试验流程提供了新方法。本文系统梳理了LLMs在药物研发全链条中的作用：揭示靶点-疾病关联、解析复杂生物医学数据、改进药物分子设计、预测药物疗效与安全性，并助力临床试验设计。该综述旨在为计算生物学、药理学及AI for Science领域的研究者与实践者提供全面洞察，展示LLMs对药物研发的潜在变革性影响。
原文摘要 · Abstract (English)
The integration of Large Language Models (LLMs) into the drug discovery and development field marks a significant paradigm shift, offering novel methodologies for understanding disease mechanisms, facilitating drug discovery, and optimizing clinical trial processes. This review highlights the expanding role of LLMs in revolutionizing various stages of the drug development pipeline. We investigate how these advanced computational models can uncover target-disease linkage, interpret complex biomedical data, enhance drug molecule design, predict drug efficacy and safety profiles, and facilitate clinical trial processes. Our paper aims to provide a comprehensive overview for researchers and practitioners in computational biology, pharmacology, and AI4Science by offering insights into the potential transformative impact of LLMs on drug discovery and development.
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