BACON: Boundary-guided Polypharmacy Side Effect Prediction via Integrating Molecular Structures and Biochemical Information

Polypharmacy side effect prediction is a vital task in healthcare machine learning, aiming to predict the occurrence of multiple side effects when patients take drugs together. Existing researches mainly follow the deep encoder-decoder paradigm and suffer from the following two limitations: 1) The encoder simply combines inadequate information about drugs; 2) The decoder fails to capture dependencies among different side effects, thus hindering accurate prediction. To overcome the aforementioned limitations, we propose a boundary-guided polypharmacy side effect prediction method (BACON). Our framework constructs two complementary views based on drug's chemical substructure and biochemical features and enhances drug representations via contrastive learning. The decoder incorporates a boundary-guided strategy to capture drug interaction dependencies for optimizing polypharmacy side effect prediction. Experimental results demonstrate BACON superiority over SOTA models in accurately predicting drug side effect events.