Computational Model Development of Drug-Target Interaction Prediction: A Review

Author(s): Qi Zhao*, Haifan Yu, Mingxuan Ji, Yan Zhao, Xing Chen*.

Journal Name: Current Protein & Peptide Science

Volume 20 , Issue 6 , 2019

Become EABM
Become Reviewer

Graphical Abstract:


In the medical field, drug-target interactions are very important for the diagnosis and treatment of diseases, they also can help researchers predict the link between biomolecules in the biological field, such as drug-protein and protein-target correlations. Therefore, the drug-target research is a very popular study in both the biological and medical fields. However, due to the limitations of manual experiments in the laboratory, computational prediction methods for drug-target relationships are increasingly favored by researchers. In this review, we summarize several computational prediction models of the drug-target connections during the past two years, and briefly introduce their advantages and shortcomings. Finally, several further interesting research directions of drug-target interactions are listed.

Keywords: Drug-target interaction prediction, computational models, drug discovery, model development, diagnosis, treatment.

Dickson, M.; Gagnon, J.P. Key factors in the rising cost of new drug discovery and development. Nat. Rev. Drug Discov., 2004, 3, 417-429.
Paul, S.M.; Mytelka, D.S.; Dunwiddie, C.T.; Persinger, C.C.; Munos, B.H.; Lindborg, S.R.; Schacht, A.L. How to improve R&D productivity: The pharmaceutical industry’s grand challenge. Nat. Rev. Drug Discov., 2010, 9, 203-214.
Kola, I.; Landis, J. Can the pharmaceutical industry reduce attrition rates? Nat. Rev. Drug Discov., 2004, 3, 711-715.
Kapetanovic, I.M. Computer-aided drug discovery and development (CADDD): In silico-chemico-biological approach. Chem. Biol. Interact., 2008, 171, 165.
Chen, 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., 2015, 17, 696.
Wu, Z.; Cheng, F.; Li, J.; Li, W.; Liu, G.; Tang, Y. SDTNBI: An integrated network and chemoinformatics tool for systematic prediction of drug-target interactions and drug repositioning. Brief. Bioinform., 2017, 18, 333-347.
Passi, A.; Rajput, N.K.; Wild, D.J.; Bhardwaj, A.; Rep, T.B. A gene ontology based drug repurposing approach for tuberculosis. J. Cheminform., 2018, 10, 24.
Peska, L.; Buza, K.; Koller, J. Drug-target interaction prediction: A Bayesian ranking approach. Comput. Methods Programs Biomed., 2017, 152, 15-21.
Zhang, W.; Chen, Y.; Li, D. Drug-target interaction prediction through label propagation with linear neighborhood information. Molecules, 2017, 22, 2056.
Lu, Y.; Guo, Y.; Korhonen, A. Link prediction in drug-target interactions network using similarity indices. BMC Bioinformatics, 2017, 18, 39.
Zong, N.; Kim, H.; Ngo, V.; Harismendy, O. Deep mining heterogeneous networks of biomedical linked data to predict novel drug-target associations. Bioinformatics, 2017, 33, 2337-2344.
Olayan, R.S.; Ashoor, H.; Bajic, V.B. DDR: Efficient computational method to predict drug-target interactions using graph mining and machine learning approaches. Bioinformatics, 2017, 373, 20140419.
Rayhan, F.; Ahmed, S.; Shatabda, S.; Farid, D.M.; Mousavian, Z.; Dehzangi, A.; Rahman, M.S. iDTI-ESBoost: Identification of drug target interaction using evolutionary and structural features with boosting. Sci. Rep., 2017, 7, 17731.
Luo, 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, 1119.
Peng, L.; Liao, B.; Zhu, W.; Li, K. Predicting drug-target interactions with multi-information fusion. IEEE J. Biomed. Health Inform., 2017, 21, 561-572.
Bolgár, B.; Antal, P.V.B-M.K-L.M.F. Fusion of drugs, targets and interactions using variational Bayesian multiple kernel logistic matrix factorization. BMC Bioinformatics, 2017, 18, 440.
Ezzat, A.; Wu, M.; Li, X.L.; Kwoh, C.K. Drug-target interaction prediction using ensemble learning and dimensionality reduction. Methods, 2017, 129, 81.
Wen, M.; Zhang, Z.; Niu, S.; Sha, H.; Yang, R.; Yun, Y.; Lu, H. Deep-learning-based drug-target interaction prediction. J. Proteome Res., 2017, 16, 1401.
Matsui, M.; Corey, D.R. Non-coding RNAs as drug targets. Nat. Rev. Drug Discov., 2016, 16, 167.
Chen, X.; Sun, Y.Z.; Zhang, D.H.; Li, J.Q.; Yan, G.Y.; An, J.Y.; You, Z.H. NRDTD: A database for clinically or experimentally supported non-coding RNAs and drug targets associations. Database, 2017, 2017, bax057.
Wang, Y.; Zeng, J. Predicting drug-target interactions using restricted Boltzmann machines. Bioinformatics, 2013, 29, 126-134.

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2019
Page: [492 - 494]
Pages: 3
DOI: 10.2174/1389203720666190123164310

Article Metrics

PDF: 37
PRC: 1