Large language models for Alzheimer's disease drug discovery

Abstract

Alzheimer's disease (AD) is a complex neurodegenerative disorder with significant global health and economic impacts. Despite decades of research, therapeutic progress has been hindered by the multifactorial nature of AD and limitations in traditional drug discovery approaches. This review explores the transformative potential of large language models (LLMs) in advancing medicinal chemistry for AD drug discovery. LLMs excel at processing and synthesizing vast biomedical datasets, enabling breakthroughs in hypothesis generation, target identification, and de novo drug design. By integrating multi-modal data, these models address key challenges, including patient heterogeneity, inefficiencies in preclinical models, and high failure rates in clinical trials. This paper highlights case studies and current implementations, including their roles in literature mining, protein structure prediction, and absorption, distribution, metabolism, excretion, and toxicity (ADME-Tox) property assessment, showcasing LLMs’ capacity to enhance drug discovery efficiency and precision. Despite challenges related to data quality, interpretability, and ethical concerns, LLMs offer a promising paradigm shift in AD research, paving the way for innovative therapeutic solutions and interdisciplinary collaboration. This review serves as a resource for fostering artificial intelligence-biomedicine integration to combat AD and improve patient outcomes.