  Interference between pharmacological substances can cause serious medical
injuries. Correctly predicting so-called drug-drug interactions (DDI) does not
only reduce these cases but can also result in a reduction of drug development
cost. Presently, most drug-related knowledge is the result of clinical
evaluations and post-marketing surveillance; resulting in a limited amount of
information. Existing data-driven prediction approaches for DDIs typically rely
on a single source of information, while using information from multiple
sources would help improve predictions. Machine learning (ML) techniques are
used, but the techniques are often unable to deal with skewness in the data.
Hence, we propose a new ML approach for predicting DDIs based on multiple data
sources. For this task, we use 12,000 drug features from DrugBank, PharmGKB,
and KEGG drugs, which are integrated using Knowledge Graphs (KGs). To train our
prediction model, we first embed the nodes in the graph using various embedding
approaches. We found that the best performing combination was a ComplEx
embedding method creating using PyTorch-BigGraph (PBG) with a
Convolutional-LSTM network and classic machine learning-based prediction
models. The model averaging ensemble method of three best classifiers yields up
to 0.94, 0.92, 0.80 for AUPR, F1-score, and MCC, respectively during 5-fold
cross-validation tests.
