College of Computer Science and Technology, Qingdao University, Ningxia Road, Qingdao, 266071, Shandong, China.
BMC Bioinformatics. 2024 Aug 23;25(1):275. doi: 10.1186/s12859-024-05904-5.
The rise of network pharmacology has led to the widespread use of network-based computational methods in predicting drug target interaction (DTI). However, existing DTI prediction models typically rely on a limited amount of data to extract drug and target features, potentially affecting the comprehensiveness and robustness of features. In addition, although multiple networks are used for DTI prediction, the integration of heterogeneous information often involves simplistic aggregation and attention mechanisms, which may impose certain limitations.
MSH-DTI, a deep learning model for predicting drug-target interactions, is proposed in this paper. The model uses self-supervised learning methods to obtain drug and target structure features. A Heterogeneous Interaction-enhanced Feature Fusion Module is designed for multi-graph construction, and the graph convolutional networks are used to extract node features. With the help of an attention mechanism, the model focuses on the important parts of different features for prediction. Experimental results show that the AUROC and AUPR of MSH-DTI are 0.9620 and 0.9605 respectively, outperforming other models on the DTINet dataset.
The proposed MSH-DTI is a helpful tool to discover drug-target interactions, which is also validated through case studies in predicting new DTIs.
网络药理学的兴起使得基于网络的计算方法在预测药物-靶标相互作用（DTI）中得到了广泛应用。然而，现有的 DTI 预测模型通常依赖于有限的数据来提取药物和靶标特征，这可能会影响特征的全面性和稳健性。此外，尽管多个网络被用于 DTI 预测，但异质信息的整合通常涉及简单的聚合和注意力机制，这可能会带来一定的局限性。
本文提出了一种用于预测药物-靶标相互作用的深度学习模型 MSH-DTI。该模型使用自监督学习方法获取药物和靶标结构特征。设计了一个异构交互增强特征融合模块用于多图构建，图卷积网络用于提取节点特征。借助注意力机制，模型对不同特征的重要部分进行预测。实验结果表明，MSH-DTI 在 DTINet 数据集上的 AUROC 和 AUPR 分别为 0.9620 和 0.9605，优于其他模型。
所提出的 MSH-DTI 是一种有助于发现药物-靶标相互作用的工具，通过预测新的 DTI 的案例研究也验证了这一点。
