Current Computer-Aided Drug DesignCurrent Computer-Aided Drug DesignCurrent Computer-Aided Drug Design1573-40991875-6697Bentham Science64449610.2174/1573409919666230713142255ChemistryResearch ArticleGraph-DTI: A New Model for Drug-target Interaction Prediction Based on Heterogenous Network Graph EmbeddingQuXiaohaninfo@benthamscience.netDuGuoxiainfo@benthamscience.netHuJinginfo@benthamscience.netCaiYongminginfo@benthamscience.netSchool of Medical Information Engineering, Guangdong Pharmaceutical University010620242061013102407012025Copyright ©; 2024, Bentham Science Publishers2024Bentham Science Publishershttps://rjeid.com/1573-4099/article/view/644496

Background:In this study, we aimed to develop a new end-to-end learning model called Graph-Drug-Target Interaction (DTI), which integrates various types of information in the heterogeneous network data, and to explore automatic learning of the topology-maintaining representations of drugs and targets, thereby effectively contributing to the prediction of DTI. Precise predictions of DTI can guide drug discovery and development. Most machine learning algorithms integrate multiple data sources and combine them with common embedding methods. However, the relationship between the drugs and target proteins is not well reported. Although some existing studies have used heterogeneous network graphs for DTI prediction, there are many limitations in the neighborhood information between the nodes in the heterogeneous network graphs. We studied the drug-drug interaction (DDI) and DTI from DrugBank Version 3.0, protein–protein interaction (PPI) from the human protein reference database Release 9, drug structure similarity from Morgan fingerprints of radius 2 and calculated by RDKit, and protein sequence similarity from Smith-Waterman score.

Method:Our study consists of three major components. First, various drugs and target proteins were integrated, and a heterogeneous network was established based on a series of data sets. Second, the graph neural networks-inspired graph auto-encoding method was used to extract high-order structural information from the heterogeneous networks, thereby revealing the description of nodes (drugs and proteins) and their topological neighbors. Finally, potential DTI prediction was made, and the obtained samples were sent to the classifier for secondary classification.

Results:The performance of Graph-DTI and all baseline methods was evaluated using the sums of the area under the precision-recall curve (AUPR) and the area under the receiver operating characteristic curve (AUC). The results indicated that Graph-DTI outperformed the baseline methods in both performance results.

Conclusion:Compared with other baseline DTI prediction methods, the results showed that Graph-DTI had better prediction performance. Additionally, in this study, we effectively classified drugs corresponding to different targets and vice versa. The above findings showed that Graph-DTI provided a powerful tool for drug research, development, and repositioning. Graph- DTI can serve as a drug development and repositioning tool more effectively than previous studies that did not use heterogeneous network graph embedding.

Deep learningdrug developmentbioinformatics databasesclassifier design and evaluationfeature representationtopology.Iskar, M.; Campillos, M.; Kuhn, M.; Jensen, L.J.; van Noort, V.; Bork, P. Drug-induced regulation of target expression. PLOS Comput. Biol., 2010, 6(9), e1000925. doi: 10.1371/journal.pcbi.1000925 PMID: 20838579Cheng, F.; Liu, C.; Jiang, J.; Lu, W.; Li, W.; Liu, G.; Zhou, W.; Huang, J.; Tang, Y. Prediction of drug-target interactions and drug repositioning via network-based inference. PLOS Comput. Biol., 2012, 8(5), e1002503. doi: 10.1371/journal.pcbi.1002503 PMID: 22589709Chen, X.; Yan, C.C.; Zhang, X.; Zhang, X.; Dai, F.; Yin, J.; Zhang, Y. Drug–target interaction prediction: Databases, web servers and computational models. Brief. Bioinform., 2016, 17(4), 696-712. doi: 10.1093/bib/bbv066 PMID: 26283676Tanoori, B.; Jahromi, M.Z.; Mansoori, E.G. Drug-target continuous binding affinity prediction using multiple sources of information. Expert Syst. Appl., 2021, 186115810. doi: 10.1016/j.eswa.2021.115810Buza, K.; Peška, L. Drug–target interaction prediction with Bipartite Local Models and hubness-aware regression. Neurocomputing, 2017, 260, 284-293. doi: 10.1016/j.neucom.2017.04.055Sharma, A.; Rani, R. BE-DTI′: Ensemble framework for drug target interaction prediction using dimensionality reduction and active learning. Comput. Methods Programs Biomed., 2018, 165, 151-162. doi: 10.1016/j.cmpb.2018.08.011 PMID: 30337070Chen, C.; Shi, H.; Jiang, Z.; Salhi, A.; Chen, R.; Cui, X.; Yu, B. DNN-DTIs: Improved drug-target interactions prediction using XGBoost feature selection and deep neural network. Comput. Biol. Med., 2021, 136104676. doi: 10.1016/j.compbiomed.2021.104676 PMID: 34375902Wang, Y.B.; You, Z.H.; Li, X.; Jiang, T.H.; Chen, X.; Zhou, X.; Wang, L. Predicting protein–protein interactions from protein sequences by a stacked sparse autoencoder deep neural network. Mol. Biosyst., 2017, 13(7), 1336-1344. doi: 10.1039/C7MB00188F PMID: 28604872Chu, X.; Lin, Y.; Wang, Y. Mlrda: A multi-task semi-supervised learning framework for drug–drug interaction prediction Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence Main track, 2019, pp. 4518-4524. doi: 10.24963/ijcai.2019/628Lim, S.; Lu, Y.; Cho, C.Y.; Sung, I.; Kim, J.; Kim, Y.; Park, S.; Kim, S. A review on compound-protein interaction prediction methods: Data, format, representation and model. Comput. Struct. Biotechnol. J., 2021, 19, 1541-1556. doi: 10.1016/j.csbj.2021.03.004 PMID: 33841755Jamali, A.A.; Kusalik, A.; Wu, F.X. MDIPA: A microRNA–drug interaction prediction approach based on non-negative matrix factorization. Bioinformatics, 2020, 36(20), 5061-5067. doi: 10.1093/bioinformatics/btaa577 PMID: 33212495Shang, Y.; Gao, L.; Zou, Q.; Yu, L. Prediction of drug-target interactions based on multi-layer network representation learning. Neurocomputing, 2021, 434, 80-89. doi: 10.1016/j.neucom.2020.12.068Zhang, Z.; Chen, L.; Zhong, F.; Wang, D.; Jiang, J.; Zhang, S.; Jiang, H.; Zheng, M.; Li, X. Graph neural network approaches for drug-target interactions. Curr. Opin. Struct. Biol., 2022, 73, 102327. doi: 10.1016/j.sbi.2021.102327 PMID: 35074533Zhang, C.; Song, D.; Huang, C. Heterogeneous graph neural network. KDD ’19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, July 2019, pp. 793-803. doi: 10.1145/3292500.3330961Xie, Y.; Yu, B.; Lv, S.; Zhang, C.; Wang, G.; Gong, M. A survey on heterogeneous network representation learning. Pattern Recognit., 2021, 116, 107936. doi: 10.1016/j.patcog.2021.107936An, Q.; Yu, L. A heterogeneous network embedding framework for predicting similarity-based drug-target interactions. Brief. Bioinform., 2021, 22(6), bbab275. doi: 10.1093/bib/bbab275 PMID: 34373895Li, J.; Wang, J.; Lv, H.; Zhang, Z.; Wang, Z. IMCHGAN: inductive matrix completion with heterogeneous graph attention networks for drug–target interactions prediction. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 2022, 19(2), 655-665. doi: 10.1109/TCBB.2021.3088614 PMID: 34115592Peng, J.; Wang, Y.; Guan, J.; Li, J.; Han, R.; Hao, J.; Wei, Z.; Shang, X. An end-to-end heterogeneous graph representation learning-based framework for drug-target interaction prediction. Brief. Bioinform., 2021, 22(5), bbaa430. doi: 10.1093/bib/bbaa430 PMID: 33517357Hamilton, W.; Ying, Z.; Leskovec, J. Inductive representation learning on large graphs. Adv. Neural Inf. Process. Syst; , 2017, pp. 1024-1034.Lü, L.; Zhou, T. Link prediction in complex networks: A survey. Physica A, 2011, 390(6), 1150-1170. doi: 10.1016/j.physa.2010.11.027Wang, Y.C.; Yang, Z.X.; Wang, Y.; Deng, N-Y. Computationally probing drug-protein interactions via support vector machine. Lett. Drug Des. Discov., 2010, 7(5), 370-378. doi: 10.2174/157018010791163433Knox, C.; Law, V.; Jewison, T.; Liu, P.; Ly, S.; Frolkis, A.; Pon, A.; Banco, K.; Mak, C.; Neveu, V.; Djoumbou, Y.; Eisner, R.; Guo, A.C.; Wishart, D.S. DrugBank 3.0: A comprehensive resource for ‘Omics’ research on drugs. Nucleic Acids Res., 2011, 39(S1), D1035-D1041. doi: 10.1093/nar/gkq1126 PMID: 21059682Rogers, D.; Hahn, M. Extended-connectivity fingerprints. J. Chem. Inf. Model., 2010, 50(5), 742-754. doi: 10.1021/ci100050t PMID: 20426451Landrum, G. RDKit: A software suite for cheminformatics, computational chemistry, and predictive modeling; Greg Landrum, 2013.Smith, T.F.; Waterman, M.S. Identification of common molecular subsequences. J. Mol. Biol., 1981, 147(1), 195-197. doi: 10.1016/0022-2836(81)90087-5 PMID: 7265238Zhang, Y.; Skolnick, J. Scoring function for automated assessment of protein structure template quality. Proteins, 2004, 57(4), 702-710. doi: 10.1002/prot.20264 PMID: 15476259Zhang, Z. Improved adam optimizer for deep neural networks. 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), 04-06 June 2018Banff, AB, Canada. 2018, pp. 1-2. doi: 10.1109/IWQoS.2018.8624183Zheng, X.; Ding, H.; Mamitsuka, H. Collaborative matrix factorization with multiple similarities for predicting drug–target interactions. Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, 2013, pp. 1025-1033. doi: 10.1145/2487575.2487670Wang, W.; Yang, S.; Zhang, X.; Li, J. Drug repositioning by integrating target information through a heterogeneous network model. Bioinformatics, 2014, 30(20), 2923-2930. doi: 10.1093/bioinformatics/btu403 PMID: 24974205Luo, Y.; Zhao, X.; Zhou, J.; Yang, J.; Zhang, Y.; Kuang, W.; Peng, J.; Chen, L.; Zeng, J. A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Nat. Commun., 2017, 8(1), 573. doi: 10.1038/s41467-017-00680-8 PMID: 28924171Wan, F.; Hong, L.; Xiao, A.; Jiang, T.; Zeng, J. NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug–target interactions. Bioinformatics, 2019, 35(1), 104-111. doi: 10.1093/bioinformatics/bty543 PMID: 30561548Liu, Z.; Chen, Q.; Lan, W.; Pan, H.; Hao, X.; Pan, S. GADTI: Graph autoencoder approach for DTI prediction from heterogeneous network. Front. Genet., 2021, 12, 650821. doi: 10.3389/fgene.2021.650821 PMID: 33912218Vidrio, H.; Medina, M.; González-Romo, P.; Lorenzana-Jiménez, M.; Díaz-Arista, P.; Baeza, A. Semicarbazide-sensitive amine oxidase substrates potentiate hydralazine hypotension: possible role of hydrogen peroxide. J. Pharmacol. Exp. Ther., 2003, 307(2), 497-504. doi: 10.1124/jpet.103.055350 PMID: 12970383Lamb, Y.N. Rosuvastatin/ezetimibe: A review in hypercholesterolemia. Am. J. Cardiovasc. Drugs, 2020, 20(4), 381-392. doi: 10.1007/s40256-020-00421-1Gallwitz, B. Novel therapeutic approaches in diabetes. Endocr. Dev., 2016, 31, 43-56. doi: 10.1159/000439372 PMID: 26824365Qu, X.; Du, G.; Hu, J. Graph-DTI: A new model for drug–target interaction prediction based on heterogenous network graph embedding. Curr Comput Aided Drug Des., 2023. doi: 10.21203/rs.3.rs-2106602/v1