AI drug repurposing in oncology hampered by fragmented data, curated knowledge needed

AI tools are being used to look for new cancer indications for existing biologics, but the article notes that fragmented and disparate data sets limit even the most advanced models.

Repurposing promises faster access to treatments by avoiding the long development cycle of new drugs, yet success depends on linking genomic, pathway, drug-target and clinical outcome data. The piece warns that AI agents often only spot patterns and can produce unreliable connections without trustworthy training data.

The author argues that simply adding more data will not solve the problem. Instead, building curated, integrated knowledge bases is presented as the key step to enable reliable AI-driven repurposing decisions.