How AI is Revolutionizing Drug Repurposing: Faster, Safer, and Cost-Effective Solutions (2025)

The traditional drug development process is notoriously long, costly, and fraught with high failure rates. Bringing a new drug to market can take over a decade and cost billions, with many candidates failing in late-stage clinical trials. In this challenging landscape, artificial intelligence (AI) is emerging as a transformative force, particularly in the field of drug repurposing—the practice of finding new therapeutic uses for existing medications. By leveraging AI, researchers can accelerate drug discovery timelines, reduce costs, and improve safety profiles, ultimately delivering life-saving treatments to patients faster and more efficiently.

What is Drug Repurposing and Why AI Matters

Drug repurposing capitalizes on the fact that existing drugs have already undergone extensive safety testing. This significantly reduces the risk and expense compared to developing entirely new compounds. However, identifying new indications traditionally relied on serendipity, trial-and-error, or limited retrospective clinical observations.

AI changes this paradigm by analyzing vast and complex biomedical datasets—including genomic data, protein interactions, chemical structures, clinical trial results, and real-world patient outcomes—to uncover hidden connections between drugs and diseases that humans might miss. Advanced machine learning models and natural language processing sift through scientific literature and electronic health records to predict how a drug might interact with new biological targets or pathways.

For example, during the COVID-19 pandemic, AI rapidly identified existing antiviral and anti-inflammatory drugs suitable for repurposing to combat the virus, saving crucial time in the global response. Similarly, AI has revealed potential new applications for cancer drugs in neurodegenerative diseases and cardiovascular benefits in diabetes medications, demonstrating its broad impact across therapeutic areas124.

How AI Accelerates and Improves Drug Repurposing

• Data-Driven Target Discovery: AI algorithms analyze multi-omics data and biological networks to identify novel drug-disease associations and mechanisms of action, enabling more precise targeting than traditional methods34.

• In Silico Screening and Predictive Modeling: Machine learning models simulate drug interactions with biological systems, predict efficacy, and anticipate adverse effects, reducing reliance on costly and time-consuming lab experiments12.

• Optimizing Clinical Trials: AI improves patient recruitment by identifying suitable candidates based on genetic and clinical profiles, predicts trial outcomes, and designs adaptive trial protocols, increasing the likelihood of success and reducing costs17.

• Cost and Time Efficiency: By focusing on approved drugs with known safety profiles, AI-driven repurposing can bypass early-stage toxicology studies, shortening development timelines from years to months and lowering financial risks126.

• Personalized Medicine: AI enables tailoring of repurposed drugs to specific patient subgroups, improving treatment efficacy and minimizing adverse reactions13.

Real-World Impact and Industry Adoption

The pharmaceutical industry has embraced AI-powered drug repurposing, with over 470 companies developing AI platforms to identify new indications for existing drugs. Major collaborations, such as Johnson & Johnson’s partnership with BenevolentAI, illustrate how AI accelerates redevelopment of clinical-stage candidates for new uses, like treating Parkinson’s disease-related symptoms4.

Regulatory agencies are also facilitating repurposing through streamlined approval pathways (e.g., the FDA’s 505(b)(2) route), which leverage existing safety data to expedite market entry. This regulatory support, combined with AI’s predictive power, is fueling investment, public-private partnerships, and patient advocacy efforts focused on repurposing, especially for rare diseases and precision oncology3.

Challenges and Future Outlook

Despite its promise, AI in drug repurposing faces challenges including data quality issues, prediction accuracy, and the need for explainability of AI models. Moreover, AI-generated drug candidates have yet to fully prove their effectiveness in the market, underscoring the importance of integrating computational predictions with experimental and clinical validation46.

Nevertheless, the trajectory is clear: AI is making drug repurposing faster, safer, and more cost-effective, transforming pharmaceutical R&D and expanding therapeutic options for patients worldwide. As AI technologies and data resources continue to evolve, their impact on drug repurposing will only deepen, offering hope for addressing unmet medical needs with greater speed and precision.

In summary

AI revolutionizes drug repurposing by:

• Harnessing big data and machine learning to uncover new drug uses
• Accelerating discovery and clinical testing timelines
• Reducing costs by leveraging known safety profiles
• Enhancing personalized treatment approaches
• Enabling regulatory and industry adoption for faster patient access

This convergence of AI and drug repurposing is reshaping the future of medicine, delivering faster, safer, and more affordable therapeutic solutions.