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

Abstract

In recent years, the integration of artificial intelligence (AI) into biomedical research has emerged as a transformative force, with Large Language Models (LLMs) such as GPT-4, BERT, and BioMedLM leading the forefront. These models, originally designed for natural language processing (NLP) tasks, have demonstrated remarkable versatility and capacity in processing and generating human-like text. Their utility in drug discovery is gaining significant traction, reshaping how researchers understand complex disease mechanisms, identify novel therapeutic targets, and streamline the clinical trial process.

LLMs can assimilate and interpret vast corpora of biomedical literature, clinical trial data, genomic sequences, and patient records to generate actionable insights. In disease mechanism exploration, they aid in the automatic synthesis of multi-omics data, identifying gene-disease associations, and elucidating intricate pathways involved in pathogenesis. Their context-aware nature enables them to predict disease progression, biomarker relevance, and molecular interactions that would typically require months of manual curation by domain experts.

Furthermore, in drug repurposing and target identification, LLMs have proven effective in mining unstructured literature, linking old drugs to new indications by identifying overlooked relationships. Their integration with graph neural networks and knowledge graphs further amplifies their inference capabilities. In terms of de novo drug design, LLMs support the generation of chemically plausible compounds by learning the grammar of chemical structures and biological interactions, further supported by reinforcement learning and generative modeling.

LLMs are also disrupting the clinical trial pipeline by optimizing trial protocol design, patient recruitment, and adverse event prediction. By leveraging real-world data and EHRs, these models can identify suitable candidate cohorts, forecast dropout risks, and refine trial criteria in real time. Regulatory bodies and pharmaceutical companies are increasingly exploring AI-driven tools to reduce costs and accelerate timelines without compromising safety or efficacy.

Nevertheless, challenges persist. The interpretability of LLMs remains limited, especially when deployed in critical biomedical contexts. Bias in training data, lack of standardized evaluation metrics, and regulatory hurdles continue to hinder seamless integration. Ethical considerations around patient privacy and data handling are also paramount.

In this review, we explore the multifaceted applications of LLMs in drug discovery with a focus on understanding disease mechanisms and optimizing clinical trials. We examine key models and frameworks, compare methodologies, and discuss case studies that illustrate their real-world impact. Furthermore, we highlight current limitations, ongoing advancements, and the future trajectory of LLMs in precision medicine and pharmaceutical innovation. By synthesizing current literature and empirical evidence, this review aims to provide a comprehensive perspective on the potential of LLMs to revolutionize the drug discovery landscape and facilitate data-driven healthcare transformation.

DOI: 10.8612/39.2.2024.1