Drug Discovery

"AI can also assist with more efficient drug design, predicting drug behavior, and accelerating the identification of effective drug combinations and repurposed therapies. In one study, researchers developed an ML-driven approach to guide combination drug screening by prioritizing which of the 1.4 million possible pairwise combinations of 206 drugs to test in pediatric cancer cell lines. The model accurately predicted previously unknown drug pairings and identified effective combinations confirmed by follow-up experiments after testing only 4 percent of the 1.4 million possible combinations—a task that would have taken a team of trained researchers years to complete (1051)Tosh C, et al. (2025) Nat Commun, 16: 156.. This adaptive screening strategy could help streamline the discovery of promising drug combinations and substantially shorten the timeline for future experimental validation. AI can also expand treatment options for cancer through drug repurposing—the use of existing drugs approved for non-cancer conditions to treat cancer. Researchers recently developed an ML model that uses patient-specific genomic data, including mutations in non-coding regions of the genome, to identify drugs originally intended for other diseases. By using tumor data from over 5,000 patients, this model identified actionable drugs approved to treat conditions other than cancer for more than 30 percent of cases and successfully validated several predictions in laboratory tests using tumor cells from patients with pancreatic and esophageal cancers (1053)Dong X, et al. (2025) NPJ Precis Oncol, 9: 36.. These findings highlight how AI models can help overcome long-standing barriers in drug discovery by rapidly narrowing down large experimental workflows and guiding the development of more personalized cancer treatments."