GraphMS: Drug Target Prediction Using Graph Representation Learning with Substructures

The prediction of drug–target interactions is always a key task in the field of drug redirection. However, traditional methods of predicting drug–target interactions are either mediocre or rely heavily on data stacking. In this work, we proposed our model named GraphMS. We merged heterogeneous graph information and obtained effective node information and substructure information based on mutual information in graph embeddings. We then learned high quality representations for downstream tasks, and proposed an end–to–end auto–encoder model to complete the task of link prediction. Experimental results show that our method outperforms several state–of–the–art models. The model can achieve the area under the receiver operating characteristics (AUROC) curve of 0.959 and area under the precise recall curve (AUPR) of 0.847. We found that the mutual information between the substructure and graph–level representations contributes most to the mutual information index in a relatively sparse network. And the mutual information between the node–level and graph–level representations contributes most in a relatively dense network.

[1]  C. Supuran,et al.  Antibacterial activity of ethoxzolamide against Helicobacter pylori strains SS1 and 26695 , 2020, Gut Pathogens.

[2]  Chao Che,et al.  Knowledge-Graph-Based Drug Repositioning against COVID-19 by Graph Convolutional Network with Attention Mechanism , 2021, Future Internet.

[3]  Young-Joon Kim,et al.  TET repression and increased DNMT activity synergistically induce aberrant DNA methylation. , 2020, The Journal of clinical investigation.

[4]  Jianyang Zeng,et al.  Deep learning with feature embedding for compound-protein interaction prediction , 2016, bioRxiv.

[5]  Maoguo Gong,et al.  Graph convolutional networks with multi-level coarsening for graph classification , 2020, Knowl. Based Syst..

[6]  Vipin Kumar,et al.  A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs , 1998, SIAM J. Sci. Comput..

[7]  Jean-Loup Faulon,et al.  Genome scale enzyme–metabolite and drug–target interaction predictions using the signature molecular descriptor , 2008 .

[8]  Jiajie Peng,et al.  Identifying drug-target interactions based on graph convolutional network and deep neural network , 2020, Briefings Bioinform..

[9]  Jiawei Han,et al.  Deep multiplex graph infomax: Attentive multiplex network embedding using global information , 2020, Knowl. Based Syst..

[10]  Yizhu Jiao,et al.  Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning , 2020, 2020 IEEE International Conference on Data Mining (ICDM).

[11]  Yoshihiro Yamanishi,et al.  Supervised prediction of drug–target interactions using bipartite local models , 2009, Bioinform..

[12]  Fei Wang,et al.  Knowledge-driven drug repurposing using a comprehensive drug knowledge graph , 2020, Health Informatics J..

[13]  Muhammad Attique,et al.  An intelligent healthcare monitoring framework using wearable sensors and social networking data , 2021, Future Gener. Comput. Syst..

[14]  Jian Peng,et al.  A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information , 2017, Nature Communications.

[15]  Pietro Liò,et al.  Deep Graph Infomax , 2018, ICLR.

[16]  Chee Keong Kwoh,et al.  Drug-target interaction prediction by learning from local information and neighbors , 2013, Bioinform..

[17]  Ming Wen,et al.  Deep-Learning-Based Drug-Target Interaction Prediction. , 2017, Journal of proteome research.

[18]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[19]  Vijay S. Pande,et al.  Low Data Drug Discovery with One-Shot Learning , 2016, ACS central science.

[20]  Alán Aspuru-Guzik,et al.  Convolutional Networks on Graphs for Learning Molecular Fingerprints , 2015, NIPS.

[21]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[22]  Tao Jiang,et al.  NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions , 2018, bioRxiv.

[23]  S. M. Riazul Islam,et al.  A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion , 2020, Inf. Fusion.

[24]  Ping Zhang,et al.  Interpretable Drug Target Prediction Using Deep Neural Representation , 2018, IJCAI.

[25]  Pietro Liò,et al.  Graph Attention Networks , 2017, ICLR.