  Drug-drug interactions (DDIs) are a major cause of preventable
hospitalizations and deaths. Predicting the occurrence of DDIs helps drug
safety professionals allocate investigative resources and take appropriate
regulatory action promptly. Traditional DDI prediction methods predict DDIs
based on the similarity between drugs. Recently, researchers revealed that
predictive performance can be improved by better modeling the interactions
between drug pairs with bilinear forms. However, the shallow models leveraging
bilinear forms suffer from limitations on capturing complicated nonlinear
interactions between drug pairs. To this end, we propose Multi-Label Robust
Factorization Autoencoder (abbreviated to MuLFA) for DDI prediction, which
learns a representation of interactions between drug pairs and has the
capability of characterizing complicated nonlinear interactions more precisely.
Moreover, a novel loss called CuXCov is designed to effectively learn the
parameters of MuLFA. Furthermore, the decoder is able to generate high-risk
chemical structures of drug pairs for specific DDIs, assisting pharmacists to
better understand the relationship between drug chemistry and DDI. Experimental
results on real-world datasets demonstrate that MuLFA consistently outperforms
state-of-the-art methods; particularly, it increases 21:3% predictive
performance compared to the best baseline for top 50 frequent DDIs.We also
illustrate various case studies to demonstrate the efficacy of the chemical
structures generated by MuLFA in DDI diagnosis.
