Jiang Xinchen, Wen Lu, Li Wenshui, Que Deng, Ming Lu
The National Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, China.
Hunan provincical key laboratory of Neurorestoratology, The Second Affiliated Hospital of Hunan Normal University, Changsha, China.
Front Pharmacol. 2025 Apr 28;16:1596216. doi: 10.3389/fphar.2025.1596216. eCollection 2025.
Drug target identification is a fundamental step in drug discovery and plays a pivotal role in new therapies development. Existing computational methods focus on the direct interactions between drugs and targets, often ignoring the complex interrelationships between drugs, targets and various biomolecules in the human system.
To address this limitation, we propose a novel prediction model named DTGHAT (Drug and Target Association Prediction using Heterogeneous Graph Attention Transformer based on Molecular Heterogeneous). DTGHAT utilizes a graph attention transformer to identify novel targets from 15 heterogeneous drug-gene-disease networks characterized by chemical, genomic, phenotypic, and cellular networks.
In a 5-fold cross-validation study, DTGHAT achieved an area under the receiver operating characteristic curve (AUC) of 0.9634, which is at least 4% higher than current state-of-the-art methods. Characterization ablation experiments highlight the importance of integrating biomolecular data from multiple sources in revealing drug-target interactions. In addition, a case study on cancer drugs further validates DTGHAT's effectiveness in predicting novel drug target identification. DTGHAT is free and available at: https://github.com/stella-007/DTGHAT.git.
药物靶点识别是药物研发的基本步骤，在新疗法开发中起着关键作用。现有的计算方法侧重于药物与靶点之间的直接相互作用，常常忽略了人体系统中药物、靶点和各种生物分子之间的复杂相互关系。
为解决这一局限性，我们提出了一种名为DTGHAT（基于分子异质性的使用异构图注意力变换器的药物与靶点关联预测）的新型预测模型。DTGHAT利用图注意力变换器从15个以化学、基因组、表型和细胞网络为特征的异质药物-基因-疾病网络中识别新的靶点。
在一项5折交叉验证研究中，DTGHAT在受试者工作特征曲线下面积（AUC）达到0.9634，比当前最先进的方法至少高4%。特征消融实验突出了整合来自多个来源的生物分子数据在揭示药物-靶点相互作用方面的重要性。此外，一项针对癌症药物的案例研究进一步验证了DTGHAT在预测新的药物靶点识别方面的有效性。DTGHAT可免费获取，网址为：https://github.com/stella-007/DTGHAT.git。
