Victor OK Li, SB, SM, EE, ScD; Jacqueline CK Lam, PhD; Jocelyn Downey, BSc, PhD, BA, MPhil; Illana Gozes, BSc, PhD; Yang Han, BSc, MSc, MPhil; Tushar Kaistha, BEng
Competition Sponsor: The Chinese University of Hong Kong and The University of Hong Kong
Worldwide, around 50 million people are suffering from Alzheimer’s Disease (AD) and related forms of dementia, resulting in 28.8 million disability-adjusted life-years (DALYs), posing a significant threat on human longevity and quality of life globally. To date, no effective disease-modifying treatment or preventative therapies have been found, while the search for effective drug candidates is lengthy and data-constrained. To address this challenge, we propose a novel, ground-breaking AI-driven Directed Graph Neural Network (GNN)-based Drug-Repurposing Approach, capitalizing on the association of somatic mutations in AD pathology and the identification of 272 very long gene targets. Our approach will embed the 272 protein pathway data, and make use of relevant available big genetic and drug datasets, to determine a lead combination of effective drug candidates that interacts with mutation phenotype either directly or through network-based actions. Our novelties include: (1) a directed GNN drug-repurposing approach to identify drug candidates; (2) domain-specific somatic mutations/genes incorporated into the biomedical graph to determine a lead combination of candidate drugs that interacts with somatic mutation phenotype either directly or through network-based actions; (3) domain-specific genetic directed pathways and long genes incorporated; (4) knowledge of co-morbidities of AD incorporated; (5) a lead combination of effective drugs, instead of single drugs investigated; (6) a causal model integrated to validate the lead combination of candidate drugs and confirm its impacts on genes, proteins, and behaviours, associated with AD. This longevity- and quality-of-life-driven AI drug-repurposing study will significantly accelerate the process and precision of AD drug identification.