Deep learning improves prediction of drug–drug and drug–food interactions

Significance Drug interactions, including drug–drug interactions (DDIs) and drug–food constituent interactions, can trigger unexpected pharmacological effects such as adverse drug events (ADEs). Several existing methods predict drug interactions, but require detailed, but often unavailable drug information as inputs, such as drug targets. To this end, we present a computational framework DeepDDI that accurately predicts DDI types for given drug pairs and drug–food constituent pairs using only name and structural information as inputs. We show four applications of DeepDDI to better understand drug interactions, including prediction of DDI mechanisms causing ADEs, suggestion of alternative drug members for the intended pharmacological effects without negative health effects, prediction of the effects of food constituents on interacting drugs, and prediction of bioactivities of food constituents.