School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China.
Department of Computer Science and Information Technology, La Trobe University, Melbourne 3083, Australia.
Brief Bioinform. 2021 Sep 2;22(5). doi: 10.1093/bib/bbab119.
Identifying the proteins that interact with drugs can reduce the cost and time of drug development. Existing computerized methods focus on integrating drug-related and protein-related data from multiple sources to predict candidate drug-target interactions (DTIs). However, multi-scale neighboring node sequences and various kinds of drug and protein similarities are neither fully explored nor considered in decision making.
We propose a drug-target interaction prediction method, DTIP, to encode and integrate multi-scale neighbouring topologies, multiple kinds of similarities, associations, interactions related to drugs and proteins. We firstly construct a three-layer heterogeneous network to represent interactions and associations across drug, protein, and disease nodes. Then a learning framework based on fully-connected autoencoder is proposed to learn the nodes' low-dimensional feature representations within the heterogeneous network. Secondly, multi-scale neighbouring sequences of drug and protein nodes are formulated by random walks. A module based on bidirectional gated recurrent unit is designed to learn the neighbouring sequential information and integrate the low-dimensional features of nodes. Finally, we propose attention mechanisms at feature level, neighbouring topological level and similarity level to learn more informative features, topologies and similarities. The prediction results are obtained by integrating neighbouring topologies, similarities and feature attributes using a multiple layer CNN. Comprehensive experimental results over public dataset demonstrated the effectiveness of our innovative features and modules. Comparison with other state-of-the-art methods and case studies of five drugs further validated DTIP's ability in discovering the potential candidate drug-related proteins.
鉴定与药物相互作用的蛋白质可以降低药物开发的成本和时间。现有的计算机化方法侧重于整合来自多个来源的与药物相关和与蛋白质相关的数据，以预测候选药物 - 靶标相互作用（DTI）。然而，多尺度邻接节点序列以及各种药物和蛋白质相似性既没有得到充分探索，也没有在决策中考虑。
我们提出了一种药物 - 靶标相互作用预测方法 DTIP，用于编码和整合多尺度邻接拓扑结构，多种相似性，与药物和蛋白质相关的关联和相互作用。我们首先构建了一个三层异质网络，以表示药物，蛋白质和疾病节点之间的相互作用和关联。然后提出了一种基于全连接自动编码器的学习框架，以学习异质网络中节点的低维特征表示。其次，通过随机游走对药物和蛋白质节点的多尺度邻接序列进行公式化。设计了一个基于双向门控循环单元的模块来学习邻接序列信息并整合节点的低维特征。最后，我们在特征水平，邻接拓扑水平和相似性水平提出了注意力机制，以学习更具信息量的特征，拓扑和相似性。通过使用多层 CNN 整合邻接拓扑，相似性和特征属性来获得预测结果。在公共数据集上的综合实验结果证明了我们的创新特征和模块的有效性。与其他最先进的方法的比较以及五种药物的案例研究进一步验证了 DTIP 发现潜在候选药物相关蛋白质的能力。
