  Drug-drug interactions are preventable causes of medical injuries and often
result in doctor and emergency room visits. Computational techniques can be
used to predict potential drug-drug interactions. We approach the drug-drug
interaction prediction problem as a link prediction problem and present two
novel methods for drug-drug interaction prediction based on artificial neural
networks and factor propagation over graph nodes: adjacency matrix
factorization (AMF) and adjacency matrix factorization with propagation (AMFP).
We conduct a retrospective analysis by training our models on a previous
release of the DrugBank database with 1,141 drugs and 45,296 drug-drug
interactions and evaluate the results on a later version of DrugBank with 1,440
drugs and 248,146 drug-drug interactions. Additionally, we perform a holdout
analysis using DrugBank. We report an area under the receiver operating
characteristic curve score of 0.807 and 0.990 for the retrospective and holdout
analyses respectively. Finally, we create an ensemble-based classifier using
AMF, AMFP, and existing link prediction methods and obtain an area under the
receiver operating characteristic curve of 0.814 and 0.991 for the
retrospective and the holdout analyses. We demonstrate that AMF and AMFP
provide state of the art results compared to existing methods and that the
ensemble-based classifier improves the performance by combining various
predictors. These results suggest that AMF, AMFP, and the proposed
ensemble-based classifier can provide important information during drug
development and regarding drug prescription given only partial or noisy data.
These methods can also be used to solve other link prediction problems. Drug
embeddings (compressed representations) created when training our models using
the interaction network have been made public.
