Large Language Models in Drug Discovery: Revolutionizing Disease Mechanism Understanding and Clinical Trials

Abstract

In recent years, the field of artificial intelligence (AI) has witnessed a paradigm shift, primarily driven by the emergence and application of Large Language Models (LLMs) such as GPT, BERT, and their biomedical counterparts like BioBERT and PubMedBERT. These models, originally developed for natural language processing (NLP) tasks, have demonstrated profound capabilities in understanding complex biological and chemical contexts, making them indispensable tools in modern drug discovery. The integration of LLMs into biomedical research heralds a new era of data-driven innovation, facilitating the comprehensive understanding of disease mechanisms, target identification, compound screening, and even the optimization of clinical trials.

This review delves into the transformative role of LLMs in drug discovery, emphasizing their impact across multiple stages of the pharmaceutical pipeline. Starting from their ability to mine vast biomedical literature to generate hypotheses about disease pathways, to predicting protein-drug interactions and side effects, LLMs are now contributing to faster, more accurate, and economically viable drug development processes. A notable application is their use in de novo drug design, where models suggest novel compound structures based on learned patterns in training datasets. Moreover, LLMs help bridge interdisciplinary data silos by integrating genomic, transcriptomic, and proteomic data, thereby enabling holistic analysis.

Clinical trials, traditionally characterized by high costs and low success rates, are also witnessing innovation due to LLM-driven tools. These models enhance patient recruitment strategies by analyzing electronic health records (EHRs) and matching candidates to relevant trials with higher precision. Additionally, LLMs can assist in protocol design, monitor trial progress through natural language summaries of results, and predict trial outcomes, thus reducing time and resources expended.

While the benefits are evident, challenges such as data bias, interpretability, and regulatory concerns remain. Ensuring that LLMs are transparent, unbiased, and ethically applied in a clinical setting is paramount. Nevertheless, ongoing research and development are paving the way for more refined and clinically applicable models.

This paper presents a detailed review of the current landscape, methodology, and applications of LLMs in drug discovery and clinical trials. Through tables, figures, and recent studies, it highlights key milestones, compares model performance, and outlines future prospects. The fusion of AI and drug development promises to not only accelerate discovery but also personalize medicine in unprecedented ways.

DOI: 10.8612/40.1.2025.3