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Motivation: Combination therapies have been widely used to treat cancers.However, it is cost- and time-consuming to experimentally screen synergisticdrug pairs due to the enormous number of possible drug combinations. Thus,computational methods have become an important way to predict and prioritizesynergistic drug pairs.  Results: We proposed a Deep Tensor Factorization (DTF) model, whichintegrated a tensor factorization method and a deep neural network (DNN), topredict drug synergy. The former extracts latent features from drug synergyinformation while the latter constructs a binary classifier to predict the drugsynergy status. Compared to the tensor-based method, the DTF model performedbetter 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 theDTF model with DeepSynergy and logistic regression models and found that theDTF outperformed the logistic regression model and achieved almost the sameperformance as DeepSynergy using several typical metrics for the classificationtask. Applying the DTF model to predict missing entries in our drug-cell linetensor, we identified novel synergistic drug combinations for 10 cell linesfrom the 5 cancer types. A literature survey showed that some of thesepredicted drug synergies have been identified in vivo or in vitro. Thus, theDTF model could be valuable in silico tool for prioritizing novel synergisticdrug combinations.

DTF: Deep Tensor Factorization for Predicting Anticancer Drug Synergy