Advancements in Large Language Models ( <scp>LLMs</scp> ): Empowering Drug Discovery

Abstract

ABSTRACT In recent years, the emergence of foundation models such as GPT and BERT has driven rapid advancements in large‐scale artificial intelligence, with large language models (LLMs) becoming especially transformative. These models have shown tremendous potential in accelerating drug discovery and development, offering new tools to enhance human health and medicine. This paper provides a focused review of the application of LLMs in five key areas of drug discovery: disease‐target prediction, lead compound design and optimization, drug‐target interaction prediction, molecular property prediction, and drug–drug interaction prediction. Additionally, we examine the current limitations of LLMs in these domains and discuss potential strategies to address them. Finally, we summarize the progress to date and outline promising directions for future research and development in this rapidly evolving field. This article is categorized under: Data Science &gt; Computer Algorithms and Programming Data Science &gt; Artificial Intelligence/Machine Learning Molecular and Statistical Mechanics &gt; Molecular Interactions

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