Combination therapy with multiple drugs is a potent therapy strategy for complex diseases such as cancer, due to its therapeutic efficacy and potential for reducing side effects. However, the extensive search space of drug combinations makes it challenging to screen all combinations experimentally. To address this issue, computational methods have been developed to identify prioritized drug combinations. Recently, Convolutional Neural Networks based deep learning methods have shown great potential in this community. Although the significant progress has been achieved by existing computational models, they have overlooked the important high-level semantic information and significant chemical bond features of drugs. It is worth noting that such information is rich and it can be represented by the edges of graphs in drug combination predictions. In this work, we propose a novel Edge-based Graph Transformer, named EGTSyn, for effective anti-cancer drug combination synergy prediction. In EGTSyn, a special Edge-based Graph Neural Network (EGNN) is designed to capture the global structural information of chemicals and the important information of chemical bonds, which have been neglected by most previous studies. Furthermore, we design a Graph Transformer for drugs (GTD) that combines the EGNN module with a Transformer-architecture encoder to extract high-level semantic information of drugs.

翻译：多药物联合治疗因其疗效显著且可能降低副作用，已成为针对癌症等复杂疾病的有效治疗策略。然而，药物组合的庞大搜索空间使得通过实验筛选所有组合充满挑战。为解决这一问题，计算学方法已被开发用于识别优先药物组合。近年来，基于卷积神经网络的深度学习方法在该领域展现出巨大潜力。尽管现有计算模型取得了显著进展，但它们忽略了药物中重要的高阶语义信息及关键化学键特征。值得注意的是，这些信息在药物组合预测中可通过图的边来表征，且内容极为丰富。本研究提出了一种新型基于边的图变换器——EGTSyn，用于有效的抗癌药物组合协同预测。在EGTSyn中，我们设计了一种特殊的基于边的图神经网络（EGNN），以捕获化学物质的全局结构信息及被多数既往研究所忽视的化学键重要信息。此外，我们构建了一个药物图变换器（GTD），该模块将EGNN模块与基于Transformer架构的编码器相结合，用于提取药物的高阶语义信息。