Lin Xiaoli, Xu Shuai, Liu Xuan, Zhang Xiaolong, Hu Jing
Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China.
School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China.
Biology (Basel). 2022 Jun 27;11(7):967. doi: 10.3390/biology11070967.
The key to drug discovery is the identification of a target and a corresponding drug compound. Effective identification of drug-target interactions facilitates the development of drug discovery. In this paper, drug similarity and target similarity are considered, and graphical representations are used to extract internal structural information and intermolecular interaction information about drugs and targets. First, drug similarity and target similarity are fused using the similarity network fusion (SNF) method. Then, the graph isomorphic network (GIN) is used to extract the features with information about the internal structure of drug molecules. For target proteins, feature extraction is carried out using TextCNN to efficiently capture the features of target protein sequences. Three different divisions (CVD, CVP, CVT) are used on the standard dataset, and experiments are carried out separately to validate the performance of the model for drug-target interaction prediction. The experimental results show that our method achieves better results on AUC and AUPR. The docking results also show the superiority of the proposed model in predicting drug-target interactions.
药物发现的关键在于确定一个靶点和一种相应的药物化合物。有效识别药物与靶点的相互作用有助于药物发现的发展。本文考虑了药物相似性和靶点相似性，并使用图形表示法来提取有关药物和靶点的内部结构信息和分子间相互作用信息。首先，使用相似性网络融合（SNF）方法融合药物相似性和靶点相似性。然后，使用图同构网络（GIN）来提取具有药物分子内部结构信息的特征。对于靶蛋白，使用TextCNN进行特征提取，以有效捕获靶蛋白序列的特征。在标准数据集上使用三种不同的划分（CVD、CVP、CVT），并分别进行实验以验证模型在药物-靶点相互作用预测方面的性能。实验结果表明，我们的方法在AUC和AUPR上取得了更好的结果。对接结果也显示了所提出模型在预测药物-靶点相互作用方面的优越性。
