DeepDrug as an expert guided and AI driven drug repurposing methodology for selecting the lead combination of drugs for Alzheimer’s disease
Abstract 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...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-85947-7 |
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| author | Victor O. K. Li Yang Han Tushar Kaistha Qi Zhang Jocelyn Downey Illana Gozes Jacqueline C. K. Lam |
| author_facet | Victor O. K. Li Yang Han Tushar Kaistha Qi Zhang Jocelyn Downey Illana Gozes Jacqueline C. K. Lam |
| author_sort | Victor O. K. Li |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-73d97e1a38754b66b76f44520461c08b |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-73d97e1a38754b66b76f44520461c08b2025-01-19T12:20:10ZengNature PortfolioScientific Reports2045-23222025-01-0115112510.1038/s41598-025-85947-7DeepDrug as an expert guided and AI driven drug repurposing methodology for selecting the lead combination of drugs for Alzheimer’s diseaseVictor O. K. Li0Yang Han1Tushar Kaistha2Qi Zhang3Jocelyn Downey4Illana Gozes5Jacqueline C. K. Lam6Department of Electrical and Electronic Engineering, The University of Hong KongDepartment of Electrical and Electronic Engineering, The University of Hong KongDepartment of Electrical and Electronic Engineering, The University of Hong KongDepartment of Electrical and Electronic Engineering, The University of Hong KongDepartment of Electrical and Electronic Engineering, The University of Hong KongDepartment of Human Molecular Genetics and Biochemistry, Sackler Faculty of Medicine, Tel Aviv UniversityDepartment of Electrical and Electronic Engineering, The University of Hong KongAbstract 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.https://doi.org/10.1038/s41598-025-85947-7DeepDrugAlzheimer’s DiseaseExpert-led AI drug-repurposingGraph neural networkSomatic and germline mutationsLong genes |
| spellingShingle | Victor O. K. Li Yang Han Tushar Kaistha Qi Zhang Jocelyn Downey Illana Gozes Jacqueline C. K. Lam DeepDrug as an expert guided and AI driven drug repurposing methodology for selecting the lead combination of drugs for Alzheimer’s disease Scientific Reports DeepDrug Alzheimer’s Disease Expert-led AI drug-repurposing Graph neural network Somatic and germline mutations Long genes |
| title | DeepDrug as an expert guided and AI driven drug repurposing methodology for selecting the lead combination of drugs for Alzheimer’s disease |
| title_full | DeepDrug as an expert guided and AI driven drug repurposing methodology for selecting the lead combination of drugs for Alzheimer’s disease |
| title_fullStr | DeepDrug as an expert guided and AI driven drug repurposing methodology for selecting the lead combination of drugs for Alzheimer’s disease |
| title_full_unstemmed | DeepDrug as an expert guided and AI driven drug repurposing methodology for selecting the lead combination of drugs for Alzheimer’s disease |
| title_short | DeepDrug as an expert guided and AI driven drug repurposing methodology for selecting the lead combination of drugs for Alzheimer’s disease |
| title_sort | deepdrug as an expert guided and ai driven drug repurposing methodology for selecting the lead combination of drugs for alzheimer s disease |
| topic | DeepDrug Alzheimer’s Disease Expert-led AI drug-repurposing Graph neural network Somatic and germline mutations Long genes |
| url | https://doi.org/10.1038/s41598-025-85947-7 |
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