Polypharmacy side effect prediction based on semi-implicit graph variational auto-encoder

Polypharmacy, the use of drug combinations, is an effective approach for treating complex diseases, but it increases the risk of adverse effects. To predict novel polypharmacy side effects based on known ones, many computational methods have been proposed. However, most of them generate deterministic low-dimensional embeddings when modeling the latent space of drugs, which cannot effectively capture potential side effect associations between drugs. In this study, we present SIPSE, a novel approach for predicting polypharmacy side effects. SIPSE integrates single-drug side effect information and drug-target protein data to construct novel drug feature vectors. Leveraging a semi-implicit graph variational auto-encoder, SIPSE models known polypharmacy side effects and generates flexible latent distributions for drug nodes. SIPSE infers the current node distribution by combining the distributions of neighboring nodes with embedding noise. By sampling node embeddings from these distributions, SIPSE effectively predicts polypharmacy side effects between drugs. One key innovation of SIPSE is its incorporation of uncertainty propagation through noise embedding and neighborhood sharing, enhancing its graph analysis capabilities. Extensive experiments on a benchmark dataset of polypharmacy side effects demonstrated that SIPSE significantly outperformed five state-of-the-art methods in predicting polypharmacy side effects.