摘要
Large language models (LLMs) have emerged as powerful tools in many fields, including clinical pharmacology and translational medicine. This paper aims to provide a comprehensive primer on the applications of LLMs to these disciplines. We will explore the fundamental concepts of LLMs, their potential applications in drug discovery and development processes ranging from facilitating target identification to aiding preclinical research and clinical trial analysis, and practical use cases such as assisting with medical writing and accelerating analytical workflows in quantitative clinical pharmacology. By the end of this paper, clinical pharmacologists and translational scientists will have a clearer understanding of how to leverage LLMs to enhance their research and development efforts. Keywords: artificial intelligence, clinical pharmacology, drug development, drug discovery, large language model, translational science The advent of artificial intelligence (AI) has ushered in a new era of innovation across various fields, including drug discovery and development [[1](#cts70205-bib-0001), [2](#cts70205-bib-0002), [3](#cts70205-bib-0003)]. Large language models (LLMs), such as GPT‐4, have demonstrated remarkable capabilities in processing and generating human‐like text and programming codes, offering unprecedented opportunities to enhance several aspects of the drug discovery and development processes [[4](#cts70205-bib-0004)]. Prior reviews have underscored the importance of Natural Language Processing (NLP) in Model Informed Drug Development (MIDD) [[5](#cts70205-bib-0005)] and pharmacology [[6](#cts70205-bib-0006)]. They highlighted NLP’s role in enhancing drug discovery, clinical trials, and pharmacovigilance through functionalities like named entity recognition and relation extraction. Since the release of ChatGPT, there has been a rapid increase in both the number of LLMs [[7](#cts70205-bib-0007), [8](#cts70205-bib-0008)] and their uptake in the biomedical domains [[9](#cts70205-bib-0009)]. In [[10](#cts70205-bib-0010)], Peter Lee et al. highlight the transformative impact of GPT‐4 on healthcare, including diagnostics, personalized treatment, and medical research. It discusses practical applications and ethical considerations, showcasing AI’s potential to revolutionize patient care. Due to the anticipation that LLMs would permeate every area of health care, a thorough tutorial was developed to help the biomedical informatics community harness them effectively [[11](#cts70205-bib-0011)]….
态元视角分析
从态元视角来看，这篇关于 ‘Large Language Models in Drug Discovery’ 的技术文章深入探讨了其在AI领域的应用和发展。特别是在嵌入式AI产品和模型训练方面，’Large Language Models in Drug Discovery’ 的创新将为行业带来新的机遇和挑战。我们应关注其对未来AI行业从业者的影响，以及如何将其应用于解决实际问题。