论文标题
用于预测多种药物相互作用的图形距离神经网络
Graph Distance Neural Networks for Predicting Multiple Drug Interactions
论文作者
论文摘要
由于多药的组合被广泛应用，因此准确的药物相互作用（DDI）的准确预测变得越来越关键。在我们的方法中，我们使用图表示药物相互作用：节点代表药物；边缘代表药物 - 药物相互作用。基于我们的假设，我们将DDI的预测转换为链接预测问题，利用已知的药物节点特性和DDI类型来预测未知的DDI类型。这项工作提出了一个图形距离神经网络（GDNN），以预测药物 - 药物相互作用。首先，GDNN通过目标点方法生成节点的初始特征，完全包括图中的距离信息。其次，GDNN采用改进的消息传递框架来更好地生成每个药物节点嵌入式表达式，全面考虑节点和边缘的特征。第三，GDNN聚集了嵌入式表达式，经过MLP处理以生成最终预测的药物相互作用类型。 GDNN在OGB-DDI数据集上实现了hits@20 = 0.9037，证明GDNN可以有效地预测DDI。
Since multidrug combination is widely applied, the accurate prediction of drug-drug interaction (DDI) is becoming more and more critical. In our method, we use graph to represent drug-drug interaction: nodes represent drug; edges represent drug-drug interactions. Based on our assumption, we convert the prediction of DDI to link prediction problem, utilizing known drug node characteristics and DDI types to predict unknown DDI types. This work proposes a Graph Distance Neural Network (GDNN) to predict drug-drug interactions. Firstly, GDNN generates initial features for nodes via target point method, fully including the distance information in the graph. Secondly, GDNN adopts an improved message passing framework to better generate each drug node embedded expression, comprehensively considering the nodes and edges characteristics synchronously. Thirdly, GDNN aggregates the embedded expressions, undergoing MLP processing to generate the final predicted drug interaction type. GDNN achieved Test Hits@20=0.9037 on the ogb-ddi dataset, proving GDNN can predict DDI efficiently.
