Application of computational methods in the drug discovery and development of Alzheimer's disease

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder marked by the accumulation of amyloid plaques and neurofibrillary tangles in the brain, leading to cognitive decline. As the most common dementia, AD affects over 50 million individuals worldwide, with 10 million new cases each year. Despite many significant progresses in AD research fields, there is still no effective treatment. Advances in molecular modelling and artificial intelligence (AI) algorithm have facilitated the application of computational methods in AD drug development, offering advantages in terms of efficiency, cost-effectiveness, and resource conservation. These strategies, including molecular docking, molecular dynamics simulation, protein structure prediction, virtual screening, and de novo drug design, have been widely employed in the past decades. Additionally, AI pharmaceutical companies have revolutionized the traditional drug development pipeline such as AD biomarker discovery, accurate diagnosis and precision medicine, etc. This review offers a thorough summary of recent progresses in computational-based methodologies in the study of AD drug development, including the elucidation the pathological mechanism at the molecular level, target identification, target 3D structure prediction, lead compound discovery, de novo molecular generation, multi-target directed ligands, drug repurposing, biomarker discovery, etc., showing promising applications of molecular modelling and AI in the development of AD therapy.