Drug repurposing using artificial intelligence, molecular docking, and hybrid approaches: A comprehensive review in general diseases vs Alzheimer's disease

Background: Alzheimer's disease (AD), the most prevalent form of dementia, remains enigmatic in its origins despite the widely accepted "amyloid hypothesis," which implicates amyloid-beta peptide aggregates in its pathogenesis and progression. Despite advancements in technology and healthcare, the incidence of AD continues to rise. The traditional drug development process remains time-consuming, often taking years to bring an AD treatment to market. Drug repurposing has emerged as a promising strategy for developing cost-effective and efficient therapeutic options by identifying new uses for existing approved drugs, thus accelerating drug development.

Objectives: This study aimed to examine two key drug repurposing methodologies in general diseases and specifically in AD, which are artificial intelligent (AI) approach and molecular docking approach. In addition, the hybrid approach that integrates AI with molecular docking techniques will be explored too.

Methodology: This study systematically compiled a comprehensive collection of relevant academic articles, scientific papers, and research studies which were published up until November 2024 (as of the writing of this review paper). The final selection of papers was filtered to include studies related to Alzheimer's disease and general diseases, and then categorized into three groups: AI articles, molecular docking articles, and hybrid articles.

Results: As a result, 331 papers were identified that employed AI for drug repurposing in general diseases, and 58 papers focused specifically in AD. For molecular docking in drug repurposing, 588 papers addressed general diseases, while 46 papers were dedicated to AD. The hybrid approach combining AI and molecular docking in drug repurposing has 52 papers for general diseases and 9 for AD. A comparative review was done across the methods, results, strengths, and limitations in those studies. Challenges of drug repurposing in AD are explored and future prospects are proposed.

Discussion and conclusion: Drug repurposing emerges as a compelling and effective strategy within AD research. Both AI and molecular docking methods exhibit significant potential in this domain. AI algorithms yield more precise predictions, thus facilitating the exploration of new therapeutic avenues for existing drugs. Similarly, molecular docking techniques revolutionize drug-target interaction modelling, employing refined algorithms to screen extensive drug databases against specific target proteins. This review offers valuable insights for guiding the utilization of AI, molecular docking, or their hybrid in AD drug repurposing endeavors. The hope is to speed up the timeline of drug discovery which could improve the therapeutic approach to AD.