Drug‐target interaction (DTI) prediction is essential for new drug design and development. Constructing heterogeneous network based on diverse information about drugs, proteins and diseases provides new opportunities for DTI prediction. However, the inherent complexity, high dimensionality and noise of such a network prevent us from taking full advantage of these network characteristics. This article proposes a novel method, NGCN, to predict drug‐target interactions from an integrated heterogeneous network, from which to extract relevant biological properties and association information while maintaining the topology information. It focuses on learning the topology representation of drugs and targets to improve the performance of DTI prediction. Unlike traditional methods, it focuses on learning the low‐dimensional topology representation of drugs and targets via graph‐based convolutional neural network. NGCN achieves substantial performance improvements over other state‐of‐the‐art methods, such as a nearly 1.0% increase in AUPR value. Moreover, we verify the robustness of NGCN through benchmark tests, and the experimental results demonstrate it is an extensible framework capable of combining heterogeneous information for DTI prediction.

中文翻译：

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NGCN：通过整合来自异构网络的信息和特征学习来预测药物靶标相互作用

药物靶点相互作用（DTI）预测对于新药设计和开发至关重要。基于药物、蛋白质和疾病的多样化信息构建异构网络为 DTI 预测提供了新的机会。然而，这种网络固有的复杂性、高维性和噪声阻碍了我们充分利用这些网络特性。本文提出了一种新方法 NGCN，用于从集成异构网络预测药物与靶标的相互作用，从中提取相关的生物学特性和关联信息，同时保持拓扑信息。它专注于学习药物和靶标的拓扑表示，以提高 DTI 预测的性能。与传统方法不同，它专注于通过基于图的卷积神经网络学习药物和靶标的低维拓扑表示。与其他最先进的方法相比，NGCN 实现了显着的性能改进，例如 AUPR 值增加了近 1.0%。此外，我们通过基准测试验证了NGCN的鲁棒性，实验结果表明它是一个能够结合异构信息进行DTI预测的可扩展框架。