MOTIVATION: While drug combination therapies are a well-established concept in 
cancer treatment, identifying novel synergistic combinations is challenging due 
to the size of combinatorial space. However, computational approaches have 
emerged as a time- and cost-efficient way to prioritize combinations to test, 
based on recently available large-scale combination screening data. Recently, 
Deep Learning has had an impact in many research areas by achieving new 
state-of-the-art model performance. However, Deep Learning has not yet been 
applied to drug synergy prediction, which is the approach we present here, 
termed DeepSynergy. DeepSynergy uses chemical and genomic information as input 
information, a normalization strategy to account for input data heterogeneity, 
and conical layers to model drug synergies.
RESULTS: DeepSynergy was compared to other machine learning methods such as 
Gradient Boosting Machines, Random Forests, Support Vector Machines and Elastic 
Nets on the largest publicly available synergy dataset with respect to mean 
squared error. DeepSynergy significantly outperformed the other methods with an 
improvement of 7.2% over the second best method at the prediction of novel drug 
combinations within the space of explored drugs and cell lines. At this task, 
the mean Pearson correlation coefficient between the measured and the predicted 
values of DeepSynergy was 0.73. Applying DeepSynergy for classification of these 
novel drug combinations resulted in a high predictive performance of an AUC of 
0.90. Furthermore, we found that all compared methods exhibit low predictive 
performance when extrapolating to unexplored drugs or cell lines, which we 
suggest is due to limitations in the size and diversity of the dataset. We 
envision that DeepSynergy could be a valuable tool for selecting novel 
synergistic drug combinations.
AVAILABILITY AND IMPLEMENTATION: DeepSynergy is available via 
www.bioinf.jku.at/software/DeepSynergy.
CONTACT: klambauer@bioinf.jku.at.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics 
online.