Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trials
It addresses the problem of accelerating and improving drug development for researchers and practitioners in computational biology and pharmacology, but it is an incremental review rather than presenting new experimental findings.
This review explores how Large Language Models (LLMs) are transforming drug discovery and development by enabling novel approaches to understand disease mechanisms, design drugs, and optimize clinical trials, though it does not provide specific numerical results.
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.