  Drug combination therapy is a powerful solution for the treatment of complex
disease such as cancers due to its capability of therapeutic efficacy and
reducing side effects. Nevertheless, it is very difficult to screen all drug
combinations by experiments since the vast number of possible combinations.
Currently, computational methods, especially graph neural networks and
transformer, have been developed to discover the prioritization of drug
combinations and shown promising potentials. Despite great achievements have
been obtained by existing computational models, they all neglected high-order
semantic information of drugs and the importance of the chemical bond features,
which contained rich information and is represented by edge of graphs in drug
predictions. In this work, we present a novel model named EGTSyn (Edge-based
Graph Transformer network for drug Synergy prediction) for anti-cancer drug
synergistic effect prediction. We design an EGNN (edge-based graph neural
network) module and a GTDblock (Graph Transformer for Drugs block). EGNN is
employed to capture the global structure information of the chemicals as well
as the importance of chemical bonds that has been neglected by most of the
previous studies. GTDblock combines the EGNN module with a
Transformer-architecture encoder to extract high-order semantic information of
drugs.
