Development of a Multi‐task Graph Neural Network for Alzheimer’s Disease Drug Repurposing
Kang‐Lin Hsieh, German Plascencia‐Villa, Ko‐Hong Lin, George Perry, Xiaoqian Jiang, Yejin Kim · 2025
Read Development of a Multi‐task Graph Neural Network for Alzheimer’s Disease Drug Repurposing by Kang‐Lin Hsieh, German Plascencia‐Villa, Ko‐Hong Lin, George Perry, Xiaoqian Jiang, Yejin Kim (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11713825.txt.
Description
Finding new uses for old drugs is a faster path to Alzheimer treatments than building drugs from scratch, and computers can rank the best bets. Kang-Lin Hsieh and co-authors built a multi-task graph network for that job in this 2025 paper. The network learns from a large map of how drugs, genes, and body processes interact.
Drugs were ranked using several kinds of proof at once, from cell response patterns to past trial results. Top-ranked drug pairs were then tested in nerve cells in the lab. Some pairs cut cell stress while keeping the cells alive and in good shape. The work shows how blending many proof types can shortlist drug pairs worth testing in animals and, later, in people.
Catalog record
| CREATOR | Kang‐Lin Hsieh, German Plascencia‐Villa, Ko‐Hong Lin, George Perry, Xiaoqian Jiang, Yejin Kim |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 2 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |