Hu Jing, Hu Shuo, Xia Minghao, Zheng Kangxing, Zhang Xiaolong
School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430065, Hubei, China.
Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan, China.
BMC Med Genomics. 2025 Jan 13;18(Suppl 1):9. doi: 10.1186/s12920-024-02073-5.
Drug and protein targets affect the physiological functions and metabolic effects of the body through bonding reactions, and accurate prediction of drug-protein target interactions is crucial for drug development. In order to shorten the drug development cycle and reduce costs, machine learning methods are gradually playing an important role in the field of drug-target interactions.
Compared with other methods, regression-based drug target affinity is more representative of the binding ability. Accurate prediction of drug target affinity can effectively reduce the time and cost of drug retargeting and new drug development. In this paper, a drug target affinity prediction model (WPGraphDTA) based on power graph and word2vec is proposed.
In this model, the drug molecular features in the power graph module are extracted by a graph neural network, and then the protein features are obtained by the Word2vec method. After feature fusion, they are input into the three full connection layers to obtain the drug target affinity prediction value. We conducted experiments on the Davis and Kiba datasets, and the experimental results showed that WPGraphDTA exhibited good prediction performance.
药物和蛋白质靶点通过结合反应影响机体的生理功能和代谢效应，准确预测药物 - 蛋白质靶点相互作用对于药物研发至关重要。为了缩短药物研发周期并降低成本，机器学习方法在药物 - 靶点相互作用领域正逐渐发挥重要作用。
与其他方法相比，基于回归的药物靶点亲和力更能代表结合能力。准确预测药物靶点亲和力可有效减少药物重新靶向和新药研发的时间及成本。本文提出了一种基于幂图和词向量（word2vec）的药物靶点亲和力预测模型（WPGraphDTA）。
在该模型中，幂图模块中的药物分子特征通过图神经网络提取，然后通过词向量方法获得蛋白质特征。特征融合后，将其输入到三个全连接层以获得药物靶点亲和力预测值。我们在Davis和Kiba数据集上进行了实验，实验结果表明WPGraphDTA表现出良好的预测性能。
