  Drug combination therapy has become a increasingly promising method in the
treatment of cancer. However, the number of possible drug combinations is so
huge that it is hard to screen synergistic drug combinations through wet-lab
experiments. Therefore, computational screening has become an important way to
prioritize drug combinations. Graph neural network have recently shown
remarkable performance in the prediction of compound-protein interactions, but
it has not been applied to the screening of drug combinations. In this paper,
we proposed a deep learning model based on graph neural networks and attention
mechanism to identify drug combinations that can effectively inhibit the
viability of specific cancer cells. The feature embeddings of drug molecule
structure and gene expression profiles were taken as input to multi-layer
feedforward neural network to identify the synergistic drug combinations. We
compared DeepDDS with classical machine learning methods and other deep
learning-based methods on benchmark data set, and the leave-one-out
experimental results showed that DeepDDS achieved better performance than
competitive methods. Also, on an independent test set released by well-known
pharmaceutical enterprise AstraZeneca, DeepDDS was superior to competitive
methods by more than 16\% predictive precision. Furthermore, we explored the
interpretability of the graph attention network, and found the correlation
matrix of atomic features revealed important chemical substructures of drugs.
We believed that DeepDDS is an effective tool that prioritized synergistic drug
combinations for further wet-lab experiment validation.
