Wang Huiqing, Wang Jingjing, Dong Chunlin, Lian Yuanyuan, Liu Dan, Yan Zhiliang
College of Information and Computer, Taiyuan University of Technology, Taiyuan, China.
Dryland Agriculture Research Center, Shanxi Academy of Agricultural Sciences, Taiyuan, China.
Front Pharmacol. 2020 Jan 28;10:1592. doi: 10.3389/fphar.2019.01592. eCollection 2019.
Drug targets are biomacromolecules or biomolecular structures that bind to specific drugs and produce therapeutic effects. Therefore, the prediction of drug-target interactions (DTIs) is important for disease therapy. Incorporating multiple similarity measures for drugs and targets is of essence for improving the accuracy of prediction of DTIs. However, existing studies with multiple similarity measures ignored the global structure information of similarity measures, and required manual extraction features of drug-target pairs, ignoring the non-linear relationship among features. In this paper, we proposed a novel approach MDADTI for DTIs prediction based on MDA. MDADTI applied random walk with restart method and positive pointwise mutual information to calculate the topological similarity matrices of drugs and targets, capturing the global structure information of similarity measures. Then, MDADTI applied multimodal deep autoencoder to fuse multiple topological similarity matrices of drugs and targets, automatically learned the low-dimensional features of drugs and targets, and applied deep neural network to predict DTIs. The results of 5-repeats of 10-fold cross-validation under three different cross-validation settings indicated that MDADTI is superior to the other four baseline methods. In addition, we validated the predictions of the MDADTI in six drug-target interactions reference databases, and the results showed that MDADTI can effectively identify unknown DTIs.
药物靶点是与特定药物结合并产生治疗效果的生物大分子或生物分子结构。因此，预测药物-靶点相互作用（DTIs）对于疾病治疗至关重要。结合药物和靶点的多种相似性度量对于提高DTIs预测的准确性至关重要。然而，现有的多种相似性度量研究忽略了相似性度量的全局结构信息，并且需要手动提取药物-靶点对的特征，忽略了特征之间的非线性关系。在本文中，我们提出了一种基于MDA的用于DTIs预测的新方法MDADTI。MDADTI应用带重启的随机游走方法和正点互信息来计算药物和靶点的拓扑相似性矩阵，捕捉相似性度量的全局结构信息。然后，MDADTI应用多模态深度自动编码器来融合药物和靶点的多个拓扑相似性矩阵，自动学习药物和靶点的低维特征，并应用深度神经网络来预测DTIs。在三种不同交叉验证设置下进行的10折交叉验证的5次重复结果表明，MDADTI优于其他四种基线方法。此外，我们在六个药物-靶点相互作用参考数据库中验证了MDADTI的预测结果，结果表明MDADTI可以有效地识别未知的DTIs。
