  Drug combination therapy is a well-established strategy for disease treatment
with better effectiveness and less safety degradation. However, identifying
novel drug combinations through wet-lab experiments is resource intensive due
to the vast combinatorial search space. Recently, computational approaches,
specifically deep learning models have emerged as an efficient way to discover
synergistic combinations. While previous methods reported fair performance,
their models usually do not take advantage of multi-modal data and they are
unable to handle new drugs or cell lines. In this study, we collected data from
various datasets covering various drug-related aspects. Then, we take advantage
of large-scale pre-training models to generate informative representations and
features for drugs, proteins, and diseases. Based on that, a message-passing
graph is built on top to propagate information together with graph structure
learning flexibility. This is first introduced in the biological networks and
enables us to generate pseudo-relations in the graph. Our framework achieves
state-of-the-art results in comparison with other deep learning-based methods
on synergistic prediction benchmark datasets. We are also capable of
inferencing new drug combination data in a test on an independent set released
by AstraZeneca, where 10% of improvement over previous methods is observed. In
addition, we're robust against unseen drugs and surpass almost 15% AU ROC
compared to the second-best model. We believe our framework contributes to both
the future wet-lab discovery of novel drugs and the building of promising
guidance for precise combination medicine.
