AI Drug Repurposing

Drug repurposing asks whether a compound developed or used for one purpose could have value for another disease. AI can rank candidates from gene-expression signatures, chemical similarity, target interactions, networks, or biomedical text. A computational pipeline may help search large evidence spaces, but a predicted association does not establish efficacy or safety for a new indication. DrugPipe is a published generative-AI-assisted virtual-screening workflow that identifies candidate compounds in a target-centric repurposing task. Its computational predictions illustrate a research strategy, not evidence that a drug is ready for patients. Researchers must determine whether a candidate binds or modulates the proposed target, whether the mechanism is relevant to the disease, and whether exposure at a safe dose is feasible. Existing approval or prior human use does not guarantee that a different dose, population, combination, or duration is safe. Repurposing still requires experimental and clinical evaluation. Teams may use biochemical and cellular assays, disease models, pharmacokinetic analysis, and appropriately designed trials. They should examine confounding in observational data, ensure that treatment timing and disease severity are handled carefully, and compare against a suitable control. An AI-ranked compound is a hypothesis that can save search effort; patients should not start or stop a medicine for a new purpose without qualified medical guidance. New indications can require different endpoints and may expose patient groups to risks that were not prominent in the original use. Researchers also consider intellectual-property, manufacturing, and supply constraints when deciding whether a candidate can be developed.