药物发现和开发中的大型语言模型:从疾病机理到临床试验

Yizhen Zheng, Huan Yee Koh, Maddie Yang, Li Li, Lauren T. May, Geoffrey I. Webb, Shirui Pan, George Church
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引用次数: 0

摘要

大语言模型(LLMs)融入药物发现和开发领域标志着一个重大的范式转变,为理解疾病机理、促进药物发现和优化临床试验过程提供了新颖的方法。本综述强调了 LLM 在彻底改变药物开发流水线各个阶段中不断扩大的作用。我们研究了这些先进的计算模型如何揭示靶点与疾病的联系、解释复杂的生物医学数据、增强药物分子设计、预测药物疗效和安全性概况以及促进临床试验过程。我们的论文旨在为计算生物学、药理学和人工智能科学(AI4Science)领域的研究人员和从业人员提供一个全面的概述,深入探讨LLMs 对药物发现和开发的潜在变革性影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trials
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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