DeepDrug: An Expert-led Domain-specific AI-Driven Drug-Repurposing Mechanism for Selecting the Lead Combination of Drugs for Alzheimer’s Disease
Open accessAbstract
Alzheimer’s Disease (AD) significantly aggravates human dignity and quality of life. While newly approved amyloid immunotherapy has been reported, effective AD drugs remain to be identified. Here, we propose a novel AI-driven drug-repurposing method, DeepDrug, to identify a lead combination of approved drugs to treat AD patients. DeepDrug advances drug-repurposing methodology in four aspects. Firstly, it incorporates expert knowledge to extend candidate targets to include long genes, immunological and aging pathways, and somatic mutation markers that are associated with AD. Secondly, it incorporates a signed directed heterogeneous biomedical graph encompassing a rich set of nodes and edges, and node/edge weighting to capture crucial pathways associated with AD. Thirdly, it encodes the weighted biomedical graph through a Graph Neural Network into a new embedding space to capture the granular relationships across different nodes. Fourthly, it systematically selects the high-order drug combinations via diminishing return-based thresholds. A five-drug lead combination, consisting of Tofacitinib, Niraparib, Baricitinib, Empagliflozin, and Doxercalciferol, has been selected from the top drug candidates based on DeepDrug scores to achieve the maximum synergistic effect. These five drugs target neuroinflammation, mitochondrial dysfunction, and glucose metabolism, which are all related to AD pathology. DeepDrug offers a novel AI-and-big-data, expert-guided mechanism for new drug combination discovery and drug-repurposing across AD and other neuro-degenerative diseases, with immediate clinical applications.
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DeepDrug identifies synergistic therapeutic combinations targeting neuroinflammation and mitochondrial dysfunction.
“DeepDrug identifies synergistic therapeutic combinations targeting neuroinflammation and mitochondrial dysfunction390, while MultiDCP models dose-dependent gene expression changes, enabling the stratification of patient subgroups based on predicted molecular responses338.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [390]
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- Authors
- Li, V. O., Han, Y., Kaistha, T., Zhang, Q., Downey, J., Gozes, I., & Lam, J. C.
- Affiliations
- University of Hong Kong (HK)
- Tel Aviv University (IL)
- Corresponding author
- Li, Victor OK
- Lam, Jacqueline CK
- Venue
- medRxiv
- Publisher
- openRxiv
- Published
- 2024-07-07
- OpenAlex
- W4400399143
- Cited by
- 8
- References
- 9
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- medRxiv (acceptedVersion)
- Funders
- Biogen
- University of Hong Kong
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Pharmacology › Cholinesterase and Neurodegenerative Diseases
- Keywords
- Alzheimer's disease, drug repurposing, expert knowledge integration, graph neural networks, diminishing returns, synergistic drug combination, neuroinflammation, mitochondrial dysfunction, glucose metabolism, tofacitinib, niraparib, baricitinib, empagliflozin, doxercalciferol, neurodegenerative diseases
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