A survey of LLMs in drug discovery and precision medicine
TL;DR
This comprehensive review examines the state-of-the-art applications of LLMs across the drug development pipeline, spanning target identification, molecular generation, property prediction, and drug repurposing in drug discovery, as well as clinical decision support, patient stratification, treatment personalization, and genomic interpretation in precision medicine.
Abstract
The convergence of artificial intelligence and biomedical research has catalyzed emerging impact in drug discovery and precision medicine. Large language models (LLMs), originally developed for natural language processing, have emerged as powerful tools capable of processing complex biomedical data, from molecular structures to clinical records. This comprehensive review examines the state-of-the-art applications of LLMs across the drug development pipeline, spanning target identification, molecular generation, property prediction, and drug repurposing in drug discovery, as well as clinical decision support, patient stratification, treatment personalization, and genomic interpretation in precision medicine. We analyze the technical methodologies underlying these applications, including multi-modal architectures, knowledge-guided approaches, and retrieval augmented generation systems. Through examination of recent advances, we highlight key achievements such as end-to-end drug discovery pipelines, multi-agent systems for clinical simulation, and knowledge-enhanced models achieving state-of-the-art performance. We critically assess current challenges, including data privacy, model interpretability, hallucination risks, and ethical considerations. Finally, we discuss future directions, emphasizing the potential for federated learning, explainable artificial intelligence, and integrated multiscale approaches to advance the field toward more reliable, transparent, and clinically applicable systems.