  Polypharmacy, the concurrent use of multiple drugs to treat a single
condition, is common in patients managing multiple or complex conditions.
However, as more drugs are added to the treatment plan, the risk of adverse
drug events (ADEs) rises rapidly. Many serious ADEs associated with
polypharmacy only become known after the drugs are in use. It is impractical to
test every possible drug combination during clinical trials. This issue is
particularly prevalent among older adults with cardiovascular disease (CVD)
where polypharmacy and ADEs are commonly observed. In this research, our
primary objective was to identify key drug features to build and evaluate a
model for modeling polypharmacy ADEs. Our secondary objective was to assess our
model on a domain-specific case study. We developed a two-layer neural network
that incorporated drug features such as molecular structure, drug-protein
interactions, and mono drug side effects (DrIVeNN). We assessed DrIVeNN using
publicly available side effect databases and determined Principal Component
Analysis (PCA) with a variance threshold of 0.95 as the most effective feature
selection method. DrIVeNN performed moderately better than state-of-the-art
models like RESCAL, DEDICOM, DeepWalk, Decagon, DeepDDI, KGDDI, and KGNN in
terms of AUROC for the drug-drug interaction prediction task. We also conducted
a domain-specific case study centered on the treatment of cardiovascular
disease (CVD). When the best performing model architecture was applied to the
CVD treatment cohort, there was a significant increase in performance from the
general model. We observed an average AUROC for CVD drug pair prediction
increasing from 0.826 (general model) to 0.975 (CVD specific model). Our
findings indicate the strong potential of domain-specific models for improving
the accuracy of drug-drug interaction predictions.
