2024/02/08 by Yunguang Qiu, Feixiong Cheng · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Medicine · #Bioinformatics and Genomic Networks #Cholinesterase and Neurodegenerative Diseases #Computational Drug Discovery Methods
paper · doi:10.1016/j.sbi.2024.102776
openalex publication_date 2024/02/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
The complex molecular mechanism and pathophysiology of Alzheimer's disease (AD) limits the development of effective therapeutics or prevention strategies. Artificial Intelligence (AI)-guided drug discovery combined with genetics/multi-omics (genomics, epigenomics, transcriptomics, proteomics, and metabolomics) analysis contributes to the understanding of the pathophysiology and precision medicine of the disease, including AD and AD-related dementia. In this review, we summarize the AI-driven methodologies for AD-agnostic drug discovery and development, including de novo drug design, virtual screening, and prediction of drug-target interactions, all of which have shown potentials. In particular, AI-based drug repurposing emerges as a compelling strategy to identify new indications for existing drugs for AD. We provide several emerging AD targets from human genetics and multi-omics findings and highlight recent AI-based technologies and their applications in drug discovery using AD as a prototypical example. In closing, we discuss future challenges and directions in AI-based drug discovery for AD and other neurodegenerative diseases.