Accurate prediction of drug-target interactions (DTIs) through biological data can reduce the time and economic cost of drug development. The prediction method of DTIs based on a similarity network is attracting increasing attention. Currently, many studies have focused on predicting DTIs. However, such approaches do not consider the features of drugs and targets in multiple networks or how to extract and merge them. In this study, we proposed a Network EmbeDding framework in mulTiPlex networks (NEDTP) to predict DTIs. NEDTP builds a similarity network of nodes based on 15 heterogeneous information networks. Next, we applied a random walk to extract the topology information of each node in the network and learn it as a low-dimensional vector. Finally, the Gradient Boosting Decision Tree model was constructed to complete the classification task. NEDTP achieved accurate results in DTI prediction, showing clear advantages over several state-of-the-art algorithms. The prediction of new DTIs was also verified from multiple perspectives. In addition, this study also proposes a reasonable model for the widespread negative sampling problem of DTI prediction, contributing new ideas to future research. Code and data are available at https://github.com/LiangYu-Xidian/NEDTP.

中文翻译：

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用于预测基于相似性的药物-靶标相互作用的异构网络嵌入框架

通过生物数据准确预测药物-靶标相互作用 (DTI) 可以减少药物开发的时间和经济成本。基于相似性网络的 DTI 预测方法越来越受到关注。目前，许多研究都集中在预测 DTI。然而，这种方法没有考虑多个网络中药物和目标的特征，也没有考虑如何提取和合并它们。在这项研究中，我们提出了一种多路复用网络 (NEDTP) 中的网络嵌入框架来预测 DTI。NEDTP基于15个异构信息网络构建节点相似网络。接下来，我们应用随机游走来提取网络中每个节点的拓扑信息，并将其作为低维向量进行学习。最后构建梯度提升决策树模型完成分类任务。NEDTP 在 DTI 预测中取得了准确的结果，与几种最先进的算法相比具有明显的优势。新DTI的预测也从多个角度得到验证。此外，本研究还为广泛存在的 DTI 预测负抽样问题提出了一个合理的模型，为未来的研究提供了新的思路。代码和数据可在 https://github.com/LiangYu-Xidian/NEDTP 获得。