  Drug combinations are frequently used for the treatment of cancer patients in
order to increase efficacy, decrease adverse side effects, or overcome drug
resistance. Given the enormous number of drug combinations, it is cost- and
time-consuming to screen all possible drug pairs experimentally. Currently, it
has not been fully explored to integrate multiple networks to predict
synergistic drug combinations using recently developed deep learning
technologies. In this study, we proposed a Graph Convolutional Network (GCN)
model to predict synergistic drug combinations in particular cancer cell lines.
Specifically, the GCN method used a convolutional neural network model to do
heterogeneous graph embedding, and thus solved a link prediction task. The
graph in this study was a multimodal graph, which was constructed by
integrating the drug-drug combination, drug-protein interaction, and
protein-protein interaction networks. We found that the GCN model was able to
correctly predict cell line-specific synergistic drug combinations from a large
heterogonous network. The majority (30) of the 39 cell line-specific models
show an area under the receiver operational characteristic curve (AUC) larger
than 0.80, resulting in a mean AUC of 0.84. Moreover, we conducted an in-depth
literature survey to investigate the top predicted drug combinations in
specific cancer cell lines and found that many of them have been found to show
synergistic antitumor activity against the same or other cancers in vitro or in
vivo. Taken together, the results indicate that our study provides a promising
way to better predict and optimize synergistic drug pairs in silico.
