  Motivation: Combination therapies have been widely used to treat cancers.
However, it is cost- and time-consuming to experimentally screen synergistic
drug pairs due to the enormous number of possible drug combinations. Thus,
computational methods have become an important way to predict and prioritize
synergistic drug pairs.
  Results: We proposed a Deep Tensor Factorization (DTF) model, which
integrated a tensor factorization method and a deep neural network (DNN), to
predict drug synergy. The former extracts latent features from drug synergy
information while the latter constructs a binary classifier to predict the drug
synergy status. Compared to the tensor-based method, the DTF model performed
better in predicting drug synergy. The area under the precision-recall curve
(PR AUC) was 0.57 for DTF and 0.24 for the tensor method. We also compared the
DTF model with DeepSynergy and logistic regression models and found that the
DTF outperformed the logistic regression model and achieved almost the same
performance as DeepSynergy using several typical metrics for the classification
task. Applying the DTF model to predict missing entries in our drug-cell line
tensor, we identified novel synergistic drug combinations for 10 cell lines
from the 5 cancer types. A literature survey showed that some of these
predicted drug synergies have been identified in vivo or in vitro. Thus, the
DTF model could be valuable in silico tool for prioritizing novel synergistic
drug combinations.

Quantitative Methods