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.

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