Exploring Landscape of Drug-Target-Pathway-Side Effect Associations

Side effects are the second and the fourth leading causes of drug attrition and death in the US. Thus, accurate prediction of side effects and understanding their mechanism of action will significantly impact drug discovery and clinical practice. Here, we show REMAP, a neighborhood-regularized weighted and imputed one-class collaborative filtering algorithm, is effective in predicting drug-side effect associations from a drug-side effect association network, and significantly outperforms the state-of-the-art multi-target learning algorithm for predicting rare side effects. We also apply FASCINATE, an extension of REMAP for multi-layered networks, to infer associations among side effects and drug targets from drug-target-side effect networks. Then, using random permutation analysis and gene overrepresentation tests, we infer statistically significant side effect-pathway associations. The predicted drug-side effect associations and side effect-causing pathways are consistent with clinical evidences. We expect more novel drug-side effect associations and side effect-causing pathways to be identified when applying REMAP and FASCINATE to large-scale chemical-gene-side effect networks.