A knowledge graph approach to discovering drug combination therapies across the phenome
Jianfeng Ke, Tingjian Ge, Rachel Dania Melamed · medRxiv · 2025
Combining two clinically approved drugs has potential to improve treatment for common disease. But, with many thousands of combinations possible, clinically testing all pairs of drugs, with all common diseases, is not feasible. Here, we propose DRACO, a new machine learning method for discovering therapeutic drug combinations. Our model leverages a foundation model describing drug biology alongside a graph derived from clinical trials. We showcase DRACO's power to answer the question: given a drug and a health condition, what second drug would create an effective combination? In that task, 80% of our predictions have been previously reported. In the harder task of distinguishing the small number of reported combinations from millions of possible candidates, DRACO ranks 99.0% of held-out drug combinations at the highest 0.1%. We expect DRACO to be a useful tool for proposing new therapies across thousands of disease phenotypes and drug candidates.