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.

本研究提出一种基于 GraphDTA 的新方法称为 GDGRU-DTA，用于预测药物和靶标之间的结合亲和力，该方法使用 GRU 和 BiGRU 来提取蛋白质序列的特征，并使用两种不同的图卷积方法来处理药物图，最终模型在两个基准数据集上表现出优异的性能。