多层次双向融合分子特征的药物-靶标结合亲和力预测
湖南工业大学, 计算机学院, 湖南株洲 412007
| 摘 要： | 传统药物开发需要经历一个漫长的过程。通过计算机准确预测药物与靶标蛋白质的结合亲和力（Drug-target binding affinity, DTA）能够极大地加快药物开发进程。准确预测DTA的关键在于如何准确地挖掘出药物和靶标的潜在特征。为解决此问题，本文提出了一个基于多通道结构融合双向靶标特征的DTA预测模型MBDTA（Multi-layer Bidirectional Drug-Target Binding Affinity），该模型利用药物与靶标的深层特征表达预测DTA。MBDTA分为三个步骤：首先，通过标签编码得到药物分子和靶标的初始特征；其次将初始特征分别送入图神经网络模块和递归神经网络模块学习其中的潜在特征；最后，将两组潜在特征整合后利用全连接层预测DTA。在KIBA数据集的实验结果表明，MBDTA在三个指标（MSE、CI和r2 m ）上的性能比当前最先进的DTA预测模型平均提升了32.42%、3.84%和23.64%。 |
| 关 键 词： | 药物-靶标结合亲和力; 图神经网络; 深度学习; 多阶邻域特征 |
| DOI： | 10.57237/j.cst.2023.04.009 |
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收稿日期: 2023-11-15
接受日期: 2023-12-13
在线出版日期: 2023-12-27
Drug-target Binding Affinity Prediction by Multi-layer Bidirectional Fusion Molecular Feature
College of Computer Science, Hunan University of Technology, Zhuzhou 412007, China
| Abstract: | Traditional drug development requires a long process. Accurate prediction of drug-target binding affinity (DTA) by computer can greatly accelerate the drug development process. The key to predicting DTA is how to accurately mine the potential features of drugs and targets. To solve this problem, this paper proposes a DTA prediction model based on a multi-layer structure fused with bidirectional target features (MBDTA). DTA is predicted by this model through exploiting the representation of deep features of the drug-target pairs. MBDTA is split into three steps: Firstly, the initial features are obtained by encoding the drug molecules and targets through label encoding; Secondly, the initial features are fed into the graph neural network module and the recurrent neural network module to learn the potential features in them, respectively; Finally, the two sets of potential features are integrated and then the DTA is predicted exploiting a fully connected layer. Experimental results on the KIBA dataset show that MBDTA improves performance by an average of 32.42%, 3.84%, and 23.64% on the three metrics, MSE, CI, and r2 m , compared to the current state-of-the-art DTA prediction model. |
| Keywords: | Drug-target Binding Affinity; Graph Neural Networks; Deep Learning; Multi-order Neighborhood Features |
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