Abstract: Traditional drug-target association prediction based on biological experiments is difficult to meet the demand of pharmaceutical research because its low efficiency and high cost. In order to solve the problem, a novel Graph Convolution for Drug-Target Interactions (GCDTI) algorithm was proposed. In GCDTI, the graph convolution and auto-encoder technology were combined by using semi-supervised learning to construct an encoding layer for integrating node features and a decoding layer for predicting full-link interactive networks respectively. At the same time, the graph convolution was used to build a latent factor model and effectively utilize the high-dimensional attribute information of drugs and targets for end-to-end learning. In this method, the input characteristic information was able to be combined with the known interaction network without preprocessing, which proved that the graph convolution layer of the model was able to effectively fuse the input data and node characteristics. Compared with other advanced methods, GCDTI has the highest prediction accuracy and average Area Under Receiver Operating Characteristic (ROC) Curve (AUC) (0.924 6±0.004 8), and has strong robustness. Experimental results show that GCDTI with the model architecture of end-to-end learning has the potential to be a reliable predictive method when large amounts of drug and target data need to be predicted.

Key words: drug-target association prediction, spectral graph convolution, computational prediction model, auto-encoder, k-fold cross validation

摘要： 传统的基于生物学实验的药物-靶标关联预测成本高、效率低，难以满足医药研发的需求。为了解决上述问题，提出一种新的基于图卷积网络的药物靶标关联预测（GCDTI）算法。GCDTI利用半监督学习方法将图卷积和自编码技术相结合，从而分别构建用于整合节点特征的编码层和用于预测全链接交互网络的解码层；同时使用图卷积技术建立潜在因子模型，并有效利用药物和靶标的高维属性信息进行端到端的学习。所提算法不需要对输入的特征信息进行任何预处理便可以将其与已知相互作用网络相结合，证明了该模型的图卷积层能够有效地融合输入数据与节点特征。与其他先进方法相比，GCDTI的预测精度和平均受试者工作特性（ROC）曲线下的面积（AUC）（0.924 6±0.004 8）最高，且具有较强的鲁棒性。实验结果表明：当需要预测大量的药物和靶标数据的关联关系时，利用端到端学习的模型架构的GCDTI有潜力成为一种可靠的预测方法。

关键词: 药物-靶标关联预测, 谱图卷积, 计算预测模型, 自编码, k折交叉验证