Centre for Bioinformatics and Intelligent Medicine, Nankai University, Tianjin 300071, China.
College of Computer Science, Nankai University, Tianjin 300071, China.
Bioinformatics. 2024 Oct 1;40(10). doi: 10.1093/bioinformatics/btae563.
Exploring the association between drugs and targets is essential for drug discovery and repurposing. Comparing with the traditional methods that regard the exploration as a binary classification task, predicting the drug-target binding affinity can provide more specific information. Many studies work based on the assumption that similar drugs may interact with the same target. These methods constructed a symmetric graph according to the undirected drug similarity or target similarity. Although these similarities can measure the difference between two molecules, it is unable to analyze the inclusion relationship of their substructure. For example, if drug A contains all the substructures of drug B, then in the message-passing mechanism of the graph neural network, drug A should acquire all the properties of drug B, while drug B should only obtain some of the properties of A.
To this end, we proposed a structure-inclusive similarity (SIS) which measures the similarity of two drugs by considering the inclusion relationship of their substructures. Based on SIS, we constructed a drug graph and a target graph, respectively, and predicted the binding affinities between drugs and targets by a graph convolutional network-based model. Experimental results show that considering the inclusion relationship of the substructure of two molecules can effectively improve the accuracy of the prediction model. The performance of our SIS-based prediction method outperforms several state-of-the-art methods for drug-target binding affinity prediction. The case studies demonstrate that our model is a practical tool to predict the binding affinity between drugs and targets.
Source codes and data are available at https://github.com/HuangStomach/SISDTA.
探索药物与靶点之间的关联对于药物发现和再利用至关重要。与传统的将探索视为二元分类任务的方法相比，预测药物-靶点结合亲和力可以提供更具体的信息。许多研究都是基于这样一种假设，即类似的药物可能与相同的靶点相互作用。这些方法根据无向药物相似性或目标相似性构建了一个对称图。虽然这些相似性可以衡量两个分子之间的差异，但它无法分析它们的子结构的包含关系。例如，如果药物 A 包含药物 B 的所有子结构，那么在图神经网络的消息传递机制中，药物 A 应该获得药物 B 的所有属性，而药物 B 应该只获得 A 的一些属性。
为此，我们提出了一种结构包容性相似性（SIS），通过考虑子结构的包含关系来衡量两个药物之间的相似性。基于 SIS，我们分别构建了一个药物图和一个目标图，并通过基于图卷积网络的模型预测药物和靶点之间的结合亲和力。实验结果表明，考虑两个分子的子结构的包含关系可以有效地提高预测模型的准确性。我们的基于 SIS 的预测方法的性能优于几种用于药物-靶点结合亲和力预测的最新方法。案例研究表明，我们的模型是预测药物和靶点之间结合亲和力的实用工具。
源代码和数据可在 https://github.com/HuangStomach/SISDTA 上获得。
