College of Mathematics and Computer Science, Dali University, Dali, 671003, China.
Yunnan Institute of Endemic Diseases Control & Prevention, Dali, 671000, China.
BMC Biol. 2024 Aug 26;22(1):182. doi: 10.1186/s12915-024-01981-3.
Accurately identifying drug-target affinity (DTA) plays a pivotal role in drug screening, design, and repurposing in pharmaceutical industry. It not only reduces the time, labor, and economic costs associated with biological experiments but also expedites drug development process. However, achieving the desired level of computational accuracy for DTA identification methods remains a significant challenge.
We proposed a novel multi-view-based graph deep model known as MvGraphDTA for DTA prediction. MvGraphDTA employed a graph convolutional network (GCN) to extract the structural features from original graphs of drugs and targets, respectively. It went a step further by constructing line graphs with edges as vertices based on original graphs of drugs and targets. GCN was also used to extract the relationship features within their line graphs. To enhance the complementarity between the extracted features from original graphs and line graphs, MvGraphDTA fused the extracted multi-view features of drugs and targets, respectively. Finally, these fused features were concatenated and passed through a fully connected (FC) network to predict DTA.
During the experiments, we performed data augmentation on all the training sets used. Experimental results showed that MvGraphDTA outperformed the competitive state-of-the-art methods on benchmark datasets for DTA prediction. Additionally, we evaluated the universality and generalization performance of MvGraphDTA on additional datasets. Experimental outcomes revealed that MvGraphDTA exhibited good universality and generalization capability, making it a reliable tool for drug-target interaction prediction.
准确识别药物-靶标亲和力（DTA）在制药行业的药物筛选、设计和再利用中起着至关重要的作用。它不仅减少了与生物实验相关的时间、劳动力和经济成本，而且还加快了药物开发过程。然而，实现 DTA 识别方法所需的计算精度水平仍然是一个重大挑战。
我们提出了一种新的基于多视图的图深度学习模型，称为 MvGraphDTA，用于 DTA 预测。MvGraphDTA 使用图卷积网络（GCN）分别从药物和靶标的原始图中提取结构特征。它更进一步，基于药物和靶标的原始图构建边作为顶点的线图。GCN 还用于提取其线图内的关系特征。为了增强从原始图和线图中提取的特征之间的互补性，MvGraphDTA 分别融合了药物和靶标的提取多视图特征。最后，这些融合的特征被串联并通过全连接（FC）网络传递，以预测 DTA。
在实验中，我们对所有使用的训练集进行了数据增强。实验结果表明，MvGraphDTA 在 DTA 预测的基准数据集上优于竞争的最先进方法。此外，我们还评估了 MvGraphDTA 在其他数据集上的通用性和泛化性能。实验结果表明，MvGraphDTA 表现出良好的通用性和泛化能力，是一种可靠的药物-靶标相互作用预测工具。
