论文标题
GDGRU-DTA：基于GNN和Double Gru预测药物靶标结合亲和力
GDGRU-DTA: Predicting Drug-Target Binding Affinity Based on GNN and Double GRU
论文作者
论文摘要
预测药物和靶向亲和力（DTA）的工作对于药物开发和重新利用至关重要。在这项工作中，我们提出了一种称为GDGRU-DTA的新方法来预测基于GraphDTA的药物和靶标之间的结合亲和力，但我们认为蛋白质序列是长序列，因此简单的CNN无法很好地捕获蛋白质序列中的上下文依赖性。因此，我们通过将蛋白质序列解释为时间序列，并使用栅极复发单元（GRU）和双向门复发单元（BIGRU）提取其特征来改进它。对于该药物，我们的处理方法与GraphDTA相似，但使用两种不同的图形卷积方法。随后，将药物和蛋白质的代表定为最终预测。我们在两个基准数据集上评估了所提出的模型。我们的模型表现优于某些最新的深度学习方法，结果证明了我们模型的可行性和出色的功能捕获能力。
The work for predicting drug and target affinity(DTA) is crucial for drug development and repurposing. In this work, we propose a novel method called GDGRU-DTA to predict the binding affinity between drugs and targets, which is based on GraphDTA, but we consider that protein sequences are long sequences, so simple CNN cannot capture the context dependencies in protein sequences well. Therefore, we improve it by interpreting the protein sequences as time series and extracting their features using Gate Recurrent Unit(GRU) and Bidirectional Gate Recurrent Unit(BiGRU). For the drug, our processing method is similar to that of GraphDTA, but uses two different graph convolution methods. Subsequently, the representation of drugs and proteins are concatenated for final prediction. We evaluate the proposed model on two benchmark datasets. Our model outperforms some state-of-the-art deep learning methods, and the results demonstrate the feasibility and excellent feature capture ability of our model.
