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 by leveraging knowledge about which health conditions each drug has been tested for. Our graph neural network model is able to generalize from a curated set of drug combinations to discover new combination therapies. We showcase our approach’s power to answer the question: given a drug and a health condition, what second drug would create an effective combination? Of our top three predicted candidate combinations, 74% have been previously reported. In the harder task of distinguishing the small number of reported combinations from millions of possible combination-condition triplets, our model ranks 94% of unseen triplets at the highest 0.1%. We expect DRACO to be a useful tool for proposing new therapies across thousands of diseases.

The authors have declared no competing interest.

This work was supported by the National Institute of General Medicine Sciences (NIGMS R35 GM151001-01) to RDM.

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