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.

翻译：药物联合疗法由于具有增强治疗效果和减少副作用的潜力，成为治疗癌症等复杂疾病的有效手段。然而，由于可能的药物组合数量巨大，通过实验筛选所有组合极其困难。当前，计算方法特别是图神经网络和Transformer已被开发用于发现药物组合的优先级，并展现出巨大潜力。尽管现有计算模型取得了显著成就，但它们都忽略了药物的高阶语义信息以及化学键特征的重要性——这些特征包含丰富信息，在药物预测中由图中的边表示。本文提出一种名为EGTSyn（基于边的图变换网络用于药物协同效应预测）的新模型，用于预测抗癌药物协同效应。我们设计了EGNN（基于边的图神经网络）模块和GTDblock（药物图变换器模块）。EGNN用于捕获化学分子的全局结构信息以及被大多数先前研究忽视的化学键重要性。GTDblock将EGNN模块与Transformer架构编码器结合，以提取药物的高阶语义信息。