IEEE J Biomed Health Inform. 2024 Nov;28(11):6997-7005. doi: 10.1109/JBHI.2024.3441324. Epub 2024 Nov 6.
Novel drug-target interaction (DTI) prediction is crucial in drug discovery and repositioning. Recently, graph neural network (GNN) has shown promising results in identifying DTI by using thresholds to construct heterogeneous graphs. However, an empirically selected threshold can lead to loss of valuable information, especially in sparse networks, a common scenario in DTI prediction. To make full use of insufficient information, we propose a DTI prediction model based on Dynamic Heterogeneous Graph (DT-DHG). And progressive learning is introduced to adjust the receptive fields of node. The experimental results show that our method significantly improves the performance of the original GNNs and is robust against the choices of backbones. Meanwhile, DT-DHG outperforms the state-of-the-art methods and effectively predicts novel DTIs.
新型药物靶点相互作用（DTI）预测在药物发现和再定位中至关重要。最近，图神经网络（GNN）通过使用阈值来构建异质图，在识别 DTI 方面显示出了有前景的结果。然而，经验选择的阈值可能导致有价值信息的丢失，特别是在稀疏网络中，稀疏网络是 DTI 预测中常见的情况。为了充分利用不足的信息，我们提出了一种基于动态异质图（DT-DHG）的 DTI 预测模型。并引入了渐进式学习来调整节点的感受野。实验结果表明，我们的方法显著提高了原始 GNN 的性能，并且对骨干的选择具有鲁棒性。同时，DT-DHG 优于最先进的方法，并有效地预测了新型 DTI。
