SDU-ANU Joint Science College, Shandong University, Weihai, 264209, Shandong, China.
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China.
Sci Rep. 2024 Oct 30;14(1):26119. doi: 10.1038/s41598-024-75742-1.
In the crucial process of drug discovery and repurposing, precise prediction of drug-target interactions (DTIs) is paramount. This study introduces a novel DTI prediction approach-Multi-Layer Graph Attention Neural Network (MLGANN), through a groundbreaking computational framework that effectively harnesses multi-source information to enhance prediction accuracy. MLGANN not only strides forward in constructing a multi-layer DTI network by capturing both direct interactions between drugs and targets as well as their multi-level information but also amalgamates Graph Convolutional Networks (GCN) with a self-attention mechanism to comprehensively integrate diverse data sources. This method exhibited significant performance surpassing existing approaches in comparative experiments, underscoring its immense potential in elevating the efficiency and accuracy of DTI predictions. More importantly, this study accentuates the significance of considering multi-source data information and network heterogeneity in the drug discovery process, offering new perspectives and tools for future pharmaceutical research.
在药物发现和再利用的关键过程中，精确预测药物-靶点相互作用（DTI）至关重要。本研究提出了一种新的 DTI 预测方法——多层图注意神经网络（MLGANN），通过一个开创性的计算框架，有效地利用多源信息来提高预测准确性。MLGANN 不仅通过构建一个多层 DTI 网络向前迈进，该网络同时捕捉药物和靶点之间的直接相互作用及其多层次信息，而且还将图卷积网络（GCN）与自注意机制相结合，以全面整合各种数据源。在对比实验中，该方法表现出显著优于现有方法的性能，突出了其在提高 DTI 预测效率和准确性方面的巨大潜力。更重要的是，本研究强调了在药物发现过程中考虑多源数据信息和网络异质性的重要性，为未来的药物研究提供了新的视角和工具。
