Liu Xiao-Huan, Lu Zhen-Hua, Wang Tao, Liu Fei
School of Biological Science, Jining Medical University, Jining, China.
College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, China.
Front Pharmacol. 2024 Dec 24;15:1458739. doi: 10.3389/fphar.2024.1458739. eCollection 2024.
The latest breakthroughs in information technology and biotechnology have catalyzed a revolutionary shift within the modern healthcare landscape, with notable impacts from artificial intelligence (AI) and deep learning (DL). Particularly noteworthy is the adept application of large language models (LLMs), which enable seamless and efficient communication between scientific researchers and AI systems. These models capitalize on neural network (NN) architectures that demonstrate proficiency in natural language processing, thereby enhancing interactions. This comprehensive review outlines the cutting-edge advancements in the application of LLMs within the pharmaceutical industry, particularly in drug development. It offers a detailed exploration of the core mechanisms that drive these models and zeroes in on the practical applications of several models that show great promise in this domain. Additionally, this review delves into the pivotal technical and ethical challenges that arise with the practical implementation of LLMs. There is an expectation that LLMs will assume a more pivotal role in the development of innovative drugs and will ultimately contribute to the accelerated development of revolutionary pharmaceuticals.
信息技术和生物技术的最新突破推动了现代医疗保健领域的革命性转变,人工智能(AI)和深度学习(DL)产生了显著影响。特别值得注意的是大语言模型(LLMs)的熟练应用,它使科研人员与人工智能系统之间能够进行无缝且高效的交流。这些模型利用在自然语言处理方面表现出色的神经网络(NN)架构,从而加强了互动。这篇全面的综述概述了大语言模型在制药行业应用中的前沿进展,尤其是在药物研发方面。它详细探讨了驱动这些模型的核心机制,并聚焦于在该领域展现出巨大潜力的几个模型的实际应用。此外,本综述深入研究了大语言模型实际应用中出现的关键技术和伦理挑战。人们期望大语言模型在创新药物开发中发挥更关键的作用,并最终推动革命性药物的加速研发。