Drug–target interaction (DTI) prediction is a crucial step in drug discovery and repositioning as it reduces experimental validation costs if done right. Thus, developing in-silico methods to predict potential DTI has become a competitive research niche, with one of its main focuses being improving the prediction accuracy. Using machine learning (ML) models for this task, specifically network-based approaches, is effective and has shown great advantages over the other computational methods. However, ML model development involves upstream hand-crafted feature extraction and other processes that impact prediction accuracy. Thus, network-based representation learning techniques that provide automated feature extraction combined with traditional ML classifiers dealing with downstream link prediction tasks may be better-suited paradigms. Here, we present such a method, DTi2Vec, which identifies DTIs using network representation learning and ensemble learning techniques. DTi2Vec constructs the heterogeneous network, and then it automatically generates features for each drug and target using the nodes embedding technique. DTi2Vec demonstrated its ability in drug–target link prediction compared to several state-of-the-art network-based methods, using four benchmark datasets and large-scale data compiled from DrugBank. DTi2Vec showed a statistically significant increase in the prediction performances in terms of AUPR. We verified the "novel" predicted DTIs using several databases and scientific literature. DTi2Vec is a simple yet effective method that provides high DTI prediction performance while being scalable and efficient in computation, translating into a powerful drug repositioning tool.

中文翻译：

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DTi2Vec：使用网络嵌入和集成学习进行药物-靶点相互作用预测

药物-靶标相互作用 (DTI) 预测是药物发现和重新定位的关键步骤，因为如果做得正确，它可以降低实验验证成本。因此，开发计算机模拟方法来预测潜在的 DTI 已成为一个有竞争力的研究领域，其主要焦点之一是提高预测精度。使用机器学习 (ML) 模型（特别是基于网络的方法）来完成此任务是有效的，并且与其他计算方法相比具有巨大的优势。然而，机器学习模型开发涉及上游手工特征提取和其他影响预测准确性的过程。因此，基于网络的表示学习技术提供自动特征提取，并与处理下游链路预测任务的传统机器学习分类器相结合，可能是更适合的范例。在这里，我们提出了一种方法 DTi2Vec，它使用网络表示学习和集成学习技术来识别 DTI。DTi2Vec 构建异构网络，然后使用节点嵌入技术自动生成每种药物和靶标的特征。与几种最先进的基于网络的方法相比，DTi2Vec 使用四个基准数据集和从 DrugBank 编译的大规模数据证明了其药物靶标链接预测的能力。DTi2Vec 在 AUPR 方面的预测性能在统计上显着增加。我们使用多个数据库和科学文献验证了“新颖”的预测 DTI。DTi2Vec 是一种简单而有效的方法，可提供高 DTI 预测性能，同时可扩展且计算高效，可转化为强大的药物重新定位工具。