Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266003, China.
Key Laboratory of Marine Drugs, Chinese Ministry of Education, School of Medicine and Pharmacy, Ocean University of China, Qingdao 266003, China.
Biomolecules. 2024 Oct 8;14(10):1267. doi: 10.3390/biom14101267.
The identification of drug-target interaction (DTI) is crucial for drug discovery. However, how to reduce the graph neural network's false positives due to its bias and negative transfer in the original bipartite graph remains to be clarified. Considering that the impact of heterogeneous auxiliary information on DTI varies depending on the drug and target, we established an adaptive enhanced personalized meta-knowledge transfer network named eta raph ssociation-Aware ontrastive earning (MGACL), which can transfer personalized heterogeneous auxiliary information from different nodes and reduce data bias. Meanwhile, we propose a novel DTI association-aware contrastive learning strategy that aligns high-frequency drug representations with learned auxiliary graph representations to prevent negative transfer. Our study improves the DTI prediction performance by about 3%, evaluated by analyzing the area under the curve (AUC) and area under the precision-recall curve (AUPRC) compared with existing methods, which is more conducive to accurately identifying drug targets for the development of new drugs.
药物-靶点相互作用（DTI）的鉴定对药物发现至关重要。然而，如何减少图神经网络由于原始二分图中的偏差和负迁移而产生的假阳性，仍有待阐明。考虑到异构辅助信息对 DTI 的影响因药物和靶点而异，我们建立了一个自适应增强个性化元知识转移网络，名为 eta 图关联感知对比学习（MGACL），它可以从不同节点转移个性化的异构辅助信息，并减少数据偏差。同时，我们提出了一种新颖的 DTI 关联感知对比学习策略，该策略对齐高频药物表示与学习辅助图表示，以防止负迁移。与现有方法相比，我们通过分析曲线下面积（AUC）和精度-召回曲线下面积（AUPRC），评估我们的研究将 DTI 预测性能提高了约 3%，这更有利于准确识别药物靶点，从而开发新药。
