Dipeptide Frequency of Word Frequency and Graph Convolutional Networks for DTA Prediction

Frontiers in Bioengineering and Biotechnology

Xianfang Wang²  Peng Gao²  Hongfei Li²  Yifeng Liu²  Fan Lu²  Dongqing Wei³ 

[1] School of Computer Science and Technology, Henan Institute of Technology, Xinxiang, China;School of Computer and Information Engineering, Henan Normal University, Xinxiang, China;School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China;

关键词: drug-target binding affinity;    dipeptide frequency of word frequency;    graph convolutional network;    variable importance measures;    deep learning;   

DOI  :  10.3389/fbioe.2020.00267

来源: DOAJ

【 摘 要 】

Deep learning is an effective method to capture drug-target binding affinity, but low accuracy is still an obstacle to be overcome. Thus, we propose a novel predictor for drug-target binding affinity based on dipeptide frequency of word frequency encoding and a hybrid graph convolutional network. Word frequency characteristics of natural language are used to improve the frequency characteristics of peptides to express target proteins. For each drug molecules, the five different features of drug atoms and the atomic bond relationships are expressed as graphs. The obtained protein features and graph structure are used as the input of convolution neural network and the input of graph convolution neural network, respectively. A prediction model is established to predict the drug affinity by calculating the hidden relationship. In the KIBA data set test experiment, the consistency coefficient of the model is 0.901, which is 0.01 higher than the existing model, and the MSE (mean square error) of the model is 0.126, which is 5% lower than the existing model. In Davis data set test experiment, the consistency coefficient of the model is 0.895, which is 0.006 higher than the existing model, and the MSE of the model is 0.220, which is 4% lower than the existing model. These results show that our proposed method can not only predict the affinity better than those existing models, but also outperform unitary deep learning approaches.

【 授权许可】

Unknown