AI And Drug Repurposing: Old Drugs, New Tricks, And The IP Questions

A fascinating review article recently published in the Annual Review of Medicine by Fu et al. caught my attention for what it signals about the future intersection of artificial intelligence and pharmaceutical development, and the intellectual property questions that may follow. “Artificial Intelligence to Guide Repurposing of Drugs” (Fu et al., Annu. Rev. Med. 2026; 77:381–398), provides a comprehensive survey of how AI and machine learning (ML) are being deployed to find new therapeutic uses for known drugs. 

The Data Explosion Driving Discovery

The premise is straightforward — we are awash in biological data. The rapid growth of multiomics datasets (genomics, transcriptomics, proteomics, metabolomics, and radiomics) together with increasingly digitized electronic health records, presents both an extraordinary opportunity and formidable challenge. The sheer volume of available data has outpaced researchers’ ability to explore it using traditional methods, and that gap is precisely where AI and ML tools are stepping in. 

Drug repurposing seeks to identify new indications for known drugs whose pharmacokinetics, safety profiles, and manufacturing processes may be already well-characterized and is promised to offer faster and less expensive paths to treatment than de novo drug development. 

Two Frameworks for Repurposing Known Drugs

Fu et al. describe two major strategic approaches. The first is target-centric drug repurposing, which rests on the premise that the same target protein may be implicated in multiple diseases; a drug acting on that target could therefore treat conditions beyond its original indication. Fu, et al. at 382. For example, Metformin is being explored for atrial fibrillation, and semaglutide, the GLP-1 receptor agonist, is being investigated for neurodegenerative diseases and substance abuse in addition to its approved diabetes and obesity indications. Id. at p. 384.

The second approach is disease-centric drug repurposing, which identifies drugs that may work across diseases sharing similar biological pathways, symptoms, or traits. A key step here is identifying overlapping mechanisms of action between the original and target diseases. The well-known example of sildenafil, originally for erectile dysfunction and pulmonary hypertension, now investigated for Alzheimer’s disease, illustrates how network-based analysis of disease modules can surface unexpected therapeutic candidates. Id. at 382.

The AI Toolkit

The computational infrastructure behind AI-driven repurposing draws on five categories of databases: chemoinformatic, bioinformatics, systems biology, multiomics, and pharmacological databases. Id. at 384-388. These provide the “AI-ready” datasets on which models are trained and evaluated.

AI subsets of machine learning and deep learning use algorithms to learn patterns from input data, and are categorized into supervised learning (labeled data required), unsupervised learning (no labels), and semi-supervised learning (partially labeled). Id. at 386. Network-based approaches, which analyze graph data representing relationships among drugs, targets, genes, and pathways, are particularly powerful in disease-centric repurposing, excelling at uncovering previously undiscovered relationships between drugs and their potential targets. Id. at 387.

Computational predictions, however promising, must be validated. The authors emphasize that experimental and clinical validation are crucial steps following AI-based repurposing, as they confirm real-world accuracy and reliability. Id. at 389. Validation approaches include iPSC-derived models (e.g., neurons from Alzheimer’s patients), animal models, and real-world evidence drawn from electronic health records or health insurance claims data. Id. at 383.

Demonstrated Success and Persistent Challenges

AI-driven repurposing has already yielded meaningful results for the treatment of Alzheimer’s disease, cardiovascular disease, cancer and COVID-19. Id. at 389-390.

Yet the challenges to widespread adoption are significant. Complex AI models demand computationally expensive hyperparameter tuning. Id. at 391. Large language models, while promising for integrating heterogeneous data, are susceptible to hallucinations and biased outputs. Id. Data security remains a concern: patient-level multiomics and clinical data require robust protections such as federated learning and interoperability standards. Id. Perhaps most critically, multiomics and clinical data arise from heterogeneous patient samples across different laboratories and health care systems, making harmonization difficult. Id. Progress will require genuine cross-disciplinary collaboration between biopharmaceutical companies, academic institutions, clinicians, and computational scientists working together. Id. 

The IP Angle: Why Patent Attorneys Should Be Paying Attention

From an intellectual property standpoint, AI-driven drug repurposing raises questions worth tracking. When an algorithm identifies a new use for an existing compound, what is the patentable invention: the method of treatment, the algorithm itself, or the data pipeline that made discovery possible? Can novel analogs and derivatives be developed? Does the repurposing require new formulations and dosing schedules? Second, medical-use claims have long been part of the pharmaceutical patent toolkit, but the involvement of AI in generating those discoveries may complicate inventorship analysis and raise questions about the sufficiency of disclosure and enablement.

There is also the question of data as a competitive asset. Fu et al. note that biopharmaceutical companies possess massive datasets that cannot be shared due to intellectual property concerns. The proprietary databases, curated knowledge graphs, and trained models underlying AI-driven repurposing represent significant intangible value potentially protectable through trade secret, patent, or a combination of strategies.

For those of us advising clients in this space, the conclusion of the Fu et al. review resonates: “AI is an indispensable aspect of the future of drug repurposing and precision medicine for challenging human diseases”. Id. at 391. The legal frameworks surrounding these discoveries will need to evolve in tandem with science.

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