What is your AI drug repurposing strategy missing?

An article from Sponsored What is your AI drug repurposing strategy missing? Already applying AI to indication expansion for oncology? Here’s what you need to know.

Published May 4, 2026 By Iman Bhattacharya Share Copy link Email LinkedIn X/Twitter Facebook Print Sponsored content By QIAGEN Digital Insights SPONSORED CONTENT BY Despite high demand for cancer treatment options, it’s becoming clear that many existing biologics aren’t being used to their full potential. Repurposing avoids the long, sometimes multi-decade process of developing and approving a new drug for a specific indication, bringing treatment options to new populations. But before you can find these opportunities, you need to connect the dots between diverse and disparate data.

AI agents seem like the perfect tool for the job, but instead of finding concrete connections, they can only identify patterns, and they’re not always right. If you're going to forward with confidence, you need to train an AI model that you can trust. The answer might not be more data If you’ve worked with oncology datasets, you already know the problem isn’t scale.

You have access to genomic profiles, pathway data, drug–target interactions, clinical outcomes and more. In fact, you probably have more data than you could ever sort through in your lifetime. So it’s no wonder that repurposing opportunities are hard to identify and clinical impact remains limited.

Most repurposing approaches rely on connecting signals across datasets, but even the best AI workflows can only work with what they’re given. It’s easy to assume that more data = better models = faster identification. With AI, you can extract associations between genes, variants, pathways, drugs and diseases on a large scale.

Unfortunately, bigger models don’t solve all problems when the foundational data are fragmented or inconsistent. This creates unreliable results – AI can't reliably fact-check the data, analyze study design or distinguish correlation from causation. So how can you trust your AI-generated insights?

Building better AI agents Meet your AI halfway by giving it structured, high-quality data as a foundation. Instead of amplifying noise, you can reveal the hidden potential of existing biologics with knowledge graphs and normalized data that map the causal relationships between genes, variants, pathways, diseases and more. This relies on data that has been manually curated by experts – someone who can sort through the oceans of existing data for you to find only the pearls.

Luckily, this person doesn’t have to be you. Share Copy link Email LinkedIn X/Twitter Facebook Print Filed Under: Pharma