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Combinatorial Chemistry & High Throughput Screening

Editor-in-Chief

ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

Research Article

Drug Target Group Prediction with Multiple Drug Networks

Author(s): Jingang Che, Lei Chen*, Zi-Han Guo, Shuaiqun Wang and Aorigele

Volume 23, Issue 4, 2020

Page: [274 - 284] Pages: 11

DOI: 10.2174/1386207322666190702103927

Price: $65

Abstract

Background: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments.

Methods: In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model.

Results and Conclusion: Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.

Keywords: Drug-target interaction, drug target group, multiple drug networks, Meka, Mulan, support vector machine.

[1]
Knowles, J.; Gromo, G. A guide to drug discovery: Target selection in drug discovery. Nat. Rev. Drug Discov., 2003, 2(1), 63-69.
[http://dx.doi.org/10.1038/nrd986] [PMID: 12509760]
[2]
Blagg, J. Structure-activity relationships for in vitro and in vivo toxicity. Annu. Rep. Med. Chem., 2006, 41, 353-368.
[http://dx.doi.org/10.1016/S0065-7743(06)41024-1]
[3]
Whitebread, S.; Hamon, J.; Bojanic, D.; Urban, L. Keynote review: in vitro safety pharmacology profiling: an essential tool for successful drug development. Drug Discov. Today, 2005, 10(21), 1421-1433.
[http://dx.doi.org/10.1016/S1359-6446(05)03632-9] [PMID: 16243262]
[4]
Campillos, M.; Kuhn, M.; Gavin, A.C.; Jensen, L.J.; Bork, P. Drug target identification using side-effect similarity. Science, 2008, 321(5886), 263-266.
[http://dx.doi.org/10.1126/science.1158140] [PMID: 18621671]
[5]
Yamanishi, Y.; Araki, M.; Gutteridge, A.; Honda, W.; Kanehisa, M. Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics, 2008, 24(13), i232-i240.
[http://dx.doi.org/10.1093/bioinformatics/ btn162] [PMID: 18586719]
[6]
Chen, X.; Liu, M.X.; Yan, G.Y. Drug-target interaction prediction by random walk on the heterogeneous network. Mol. Biosyst., 2012, 8(7), 1970-1978.
[http://dx.doi.org/10.1039/c2mb00002d] [PMID: 22538619]
[7]
Bleakley, K.; Yamanishi, Y. Supervised prediction of drug-target interactions using bipartite local models. Bioinformatics, 2009, 25(18), 2397-2403.
[http://dx.doi.org/10.1093/bioinformatics/ btp433] [PMID: 19605421]
[8]
He, Z.; Zhang, J.; Shi, X.H.; Hu, L.L.; Kong, X.; Cai, Y.D.; Chou, K.C. Predicting drug-target interaction networks based on functional groups and biological features. PLoS One, 2010, 5(3)e9603
[http://dx.doi.org/10.1371/journal.pone.0009603] [PMID: 20300175]
[9]
Chen, L.; He, Z.S.; Huang, T.; Cai, Y.D. Using compound similarity and functional domain composition for prediction of drug-target interaction networks. Med. Chem., 2010, 6(6), 388-395.
[http://dx.doi.org/10.2174/157340610793563983] [PMID: 21054276]
[10]
Ding, H.; Takigawa, I.; Mamitsuka, H.; Zhu, S. Similarity-based machine learning methods for predicting drug-target interactions: a brief review. Brief. Bioinform., 2014, 15(5), 734-747.
[http://dx.doi.org/10.1093/bib/bbt056] [PMID: 23933754]
[11]
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., 2016, 17(4), 696-712.
[http://dx.doi.org/10.1093/bib/bbv066] [PMID: 26283676]
[12]
Ezzat, A.; Wu, M.; Li, X.L.; Kwoh, C.K. Computational prediction of drug-target interactions using chemogenomic approaches: an empirical survey. Brief. Bioinform., 2019, 20(4), 1337-1357.
[http://dx.doi.org/10. 1093/bib/bby002] [PMID: 29377981]
[13]
Chen, L.; Zeng, W-M. A two-step similarity-based method for prediction of drug’s target group. Protein Pept. Lett., 2013, 20(3), 364-370.
[PMID: 23570053]
[14]
Gao, Y-F.; Chen, L.; Huang, G-H.; Zhang, T.; Feng, K-Y.; Li, H-P.; Jiang, Y. Prediction of drugs target groups based on ChEBI ontology. BioMed Res. Int., 2013, 2013, 132724
[http://dx.doi.org/10.1155/2013/132724] [PMID: 24350241]
[15]
Chen, L.; Lu, J.; Luo, X.; Feng, K-Y. Prediction of drug target groups based on chemical-chemical similarities and chemicalchemical/ protein connections. Biochim. Biophys. Acta, 2014, 1844(1 Pt B), 207-213.
[http://dx.doi.org/10.1016/j.bbapap. 2013.05.021] [PMID: 23732562]
[16]
Ogata, H.; Goto, S.; Sato, K.; Fujibuchi, W.; Bono, H.; Kanehisa, M. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res., 1999, 27(1), 29-34.
[http://dx.doi.org/10.1093/nar/27. 1.29] [PMID: 9847135]
[17]
Kuhn, M.; Szklarczyk, D.; Franceschini, A.; Campillos, M.; von Mering, C.; Jensen, L.J.; Beyer, A.; Bork, P. STITCH 2: an interaction network database for small molecules and proteins. Nucleic Acids Res., 2010, 38(Database issue), D552-D556.
[http://dx.doi.org/10.1093/nar/gkp937] [PMID: 19897548]
[18]
Kuhn, M.; von Mering, C.; Campillos, M.; Jensen, L.J.; Bork, P. STITCH: interaction networks of chemicals and proteins. Nucleic Acids Res., 2008, 36(Database issue), D684-D688.
[PMID: 18084021]
[19]
Cho, H.; Berger, B.; Peng, J. Compact integration of multi-network topology for functional analysis of genes. Cell Syst., 2016, 3(6), 540-548.e5.
[http://dx.doi.org/10.1016/j.cels.2016.10.017] [PMID: 27889536]
[20]
Cortes, C.; Vapnik, V. Support-vector networks. Mach. Learn., 1995, 20(3), 273-297.
[http://dx.doi.org/10.1007/BF00994018]
[21]
Tsoumakas, G. Vlahavas, I. Random k-Labelsets: An Ensemble Method for Multilabel Classification; Springer: Berlin, Heidelberg, 2007, pp. 406-417.
[22]
Degtyarenko, K.; de Matos, P.; Ennis, M.; Hastings, J.; Zbinden, M.; McNaught, A.; Alcántara, R.; Darsow, M.; Guedj, M.; Ashburner, M. ChEBI: a database and ontology for chemical entities of biological interest. Nucleic Acids Res., 2008, 36(Database issue)(Suppl. 1), D344-D350.
[PMID: 17932057]
[23]
Wishart, D.S.; Knox, C.; Guo, A.C.; Shrivastava, S.; Hassanali, M.; Stothard, P.; Chang, Z.; Woolsey, J. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res., 2006, 34(Database issue), D668-D672.
[http://dx.doi.org/10.1093/nar/gkj067] [PMID: 16381955]
[24]
Hu, L.L.; Chen, C.; Huang, T.; Cai, Y.D.; Chou, K.C. Predicting biological functions of compounds based on chemical-chemical interactions. PLoS One, 2011, 6(12), e29491
[http://dx.doi.org/10. 1371/journal.pone.0029491] [PMID: 22220213]
[25]
Guo, Z-H.; Chen, L.; Zhao, X. A network integration method for deciphering the types of metabolic pathway of chemicals with heterogeneous information. Comb. Chem. High Throughput Screen., 2018, 21(9), 670-680.
[http://dx.doi.org/10.2174/1386207322666181206112641] [PMID: 30520371]
[26]
Chen, L.; Zeng, W.M.; Cai, Y.D.; Feng, K.Y.; Chou, K.C. Predicting anatomical therapeutic chemical (ATC) classification of drugs by integrating chemical-chemical interactions and similarities. PLoS One, 2012, 7(4), e35254
[http://dx.doi.org/10.1371/journal.pone.0035254] [PMID: 225147 24]
[27]
Chen, L.; Chu, C.; Lu, J.; Kong, X.; Huang, T.; Cai, Y.D. A computational method for the identification of new candidate carcinogenic and non-carcinogenic chemicals. Mol. Biosyst., 2015, 11(9), 2541-2550.
[http://dx.doi.org/10.1039/C5MB00276A] [PMID: 26194467]
[28]
Chen, L.; Lu, J.; Zhang, N.; Huang, T.; Cai, Y-D. A hybrid method for prediction and repositioning of drug Anatomical Therapeutic Chemical classes. Mol. Biosyst., 2014, 10(4), 868-877.
[http://dx.doi.org/10.1039/c3mb70490d] [PMID: 24492783]
[29]
Gao, Y.F.; Chen, L.; Cai, Y.D.; Feng, K.Y.; Huang, T.; Jiang, Y. Predicting metabolic pathways of small molecules and enzymes based on interaction information of chemicals and proteins. PLoS One, 2012, 7(9), e45944
[http://dx.doi.org/10.1371/journal.pone.0045944] [PMID: 23029334]
[30]
Liu, T.; Chen, L.; Pan, X. An integrated multi-label classifier with chemical-chemical interactions for prediction of chemical toxicity effects. Comb. Chem. High Throughput Screen., 2018, 21(6), 403-410.
[http://dx.doi.org/10.2174/1386207321666180601075428] [PMID: 29852864]
[31]
Chen, L.; Chu, C.; Zhang, Y-H.; Zheng, M-Y.; Zhu, L.; Kong, X.; Huang, T. Identification of drug-drug interactions using chemical interactions. Curr. Bioinform., 2017, 12(6), 526-534.
[http://dx.doi.org/10.2174/1574893611666160618094219]
[32]
Weininger, D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci., 1988, 28(1), 31-36.
[http://dx.doi.org/10.1021/ci00057a005]
[33]
Köhler, S.; Bauer, S.; Horn, D.; Robinson, P.N. Walking the interactome for prioritization of candidate disease genes. Am. J. Hum. Genet., 2008, 82(4), 949-958.
[http://dx.doi.org/10.1016/j. ajhg.2008.02.013] [PMID: 18371930]
[34]
Chen, L.; Liu, T.; Zhao, X. Inferring anatomical therapeutic chemical (ATC) class of drugs using shortest path and random walk with restart algorithms. BBA - Molecular Basis of Disease, 2018, 1864(6, Part B), 2228-2240.
[35]
Chen, L.; Zhang, Y-H.; Zhang, Z.; Huang, T.; Cai, Y-D. Inferring novel tumor suppressor genes with a protein-protein interaction network and network diffusion algorithms. Mol. Ther. Methods Clin. Dev., 2018, 10, 57-67.
[http://dx.doi.org/10.1016/j.omtm. 2018.06.007] [PMID: 30069494]
[36]
Peng, J.; Wang, H.; Lu, J.; Hui, W.; Wang, Y.; Shang, X. Identifying term relations cross different gene ontology categories. BMC Bioinformatics, 2017, 18(Suppl. 16), 573.
[http://dx.doi.org/10.1186/s12859-017-1959-3] [PMID: 29297309]
[37]
Ma, C.Y.; Chen, Y.P.P.; Berger, B.; Liao, C.S. Identification of protein complexes by integrating multiple alignment of protein interaction networks. Bioinformatics, 2017, 33(11), 1681-1688.
[http://dx.doi.org/10.1093/bioinformatics/btx043] [PMID: 28130237]
[38]
Wang, R.; Liu, G.; Wang, C.; Su, L.; Sun, L. Predicting overlapping protein complexes based on core-attachment and a local modularity structure. BMC Bioinformatics, 2018, 19(1), 305.
[http://dx.doi.org/10.1186/s12859-018-2309-9] [PMID: 30134824]
[39]
Tranchevent, L.C.; Nazarov, P.V.; Kaoma, T.; Schmartz, G.P.; Muller, A.; Kim, S.Y.; Rajapakse, J.C.; Azuaje, F. Predicting clinical outcome of neuroblastoma patients using an integrative network-based approach. Biol. Direct, 2018, 13(1), 12.
[http://dx.doi.org/10.1186/s13062-018-0214-9] [PMID: 29880025]
[40]
Tsoumakas, G.; Spyromitros-Xioufis, E.; Vilcek, J.; Vlahavas, I. MULAN: a java library for multi-label learning. J. Mach. Learn. Res., 2011, 12, 2411-2414.
[41]
Read, J.; Reutemann, P.; Pfahringer, B.; Holmes, G. MEKA: a multi-label/multi-target extension to WEKA. J. Mach. Learn. Res., 2016, 17, 1-5.
[42]
Platt, J. Sequential Minimal Optimizaton: A Fast Algorithm for Training Support Vector Machines Advances in Kernel Methods- Support Vector Learning, 1998, 1998, 208.
[43]
Kohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection In: International Joint Conference on Artificial Intelligence, Lawrence Erlbaum Associates Ltd: 1995, pp. 1137-1145.
[44]
Zhao, X.; Chen, L.; Lu, J. A similarity-based method for prediction of drug side effects with heterogeneous information. Math. Biosci., 2018, 306, 136-144.
[http://dx.doi.org/10.1016/j.mbs.2018.09.010] [PMID: 30296417]
[45]
Chen, L.; Wang, S.; Zhang, Y.-H.; Li, J.; Xing, Z.-H.; Yang, J.; Huang, T.; Cai, Y.-D. Identify key sequence features to improve CRISPR sgRNA efficacy, 2017, 5, 26582-26590.
[http://dx.doi.org/10.1109/ ACCESS.2017.2775703]
[46]
Chen, L.; Pan, X.; Hu, X.; Zhang, Y-H.; Wang, S.; Huang, T.; Cai, Y-D. Gene expression differences among different MSI statuses in colorectal cancer. Int. J. Cancer, 2018, 143(7), 1731-1740.
[http://dx.doi.org/10.1002/ijc.31554] [PMID: 29696646]
[47]
Wang, T.; Chen, L.; Zhao, X. Prediction of drug combinations with a network embedding method. Comb. Chem. High Throughput Screen., 2018, 21(10), 789-797.
[http://dx.doi.org/10.2174/1386207322666181226170140] [PMID: 30747059]
[48]
Breiman, L. Random forests. Mach. Learn., 2001, 45(1), 5-32.
[http://dx.doi.org/10.1023/A:1010933404324]
[49]
Kandaswamy, K.K.; Chou, K-C.; Martinetz, T.; Möller, S.; Suganthan, P.N.; Sridharan, S.; Pugalenthi, G. AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties. J. Theor. Biol., 2011, 270(1), 56-62.
[http://dx.doi.org/10.1016/j.jtbi.2010.10.037] [PMID: 21056045]
[50]
Zhang, Q.; Sun, X.; Feng, K.; Wang, S.; Zhang, Y.H.; Wang, S.; Lu, L.; Cai, Y.D. Predicting citrullination sites in protein sequences using mRMR method and random forest algorithm. Comb. Chem. High Throughput Screen., 2017, 20(2), 164-173.
[http://dx.doi.org/10.2174/1386207319666161227124350] [PMID: 28029071]
[51]
Wang, S.; Zhang, Y.H.; Zhang, N.; Chen, L.; Huang, T.; Cai, Y.D. Recognizing and predicting thioether bridges formed by lanthionine and beta-methyllanthionine in lantibiotics using a random forest approach with feature selection. Comb. Chem. High Throughput Screen., 2017, 20(7), 582-593.
[http://dx.doi.org/10.2174/1386207320666170310115754] [PMID: 28294058]
[52]
Li, J.; Lu, L.; Zhang, Y.H.; Liu, M.; Chen, L.; Huang, T.; Cai, Y-D. Identification of synthetic lethality based on a functional network by using machine learning algorithms. J. Cell. Biochem., 2019, 120(1), 405-416.
[http://dx.doi.org/10.1002/jcb.27395] [PMID: 30125975]
[53]
Chen, L.; Zhang, Y-H.; Pan, X.; Liu, M.; Wang, S.; Huang, T.; Cai, Y-D. Tissue Expression Difference between mRNAs and lncRNAs. Int. J. Mol. Sci., 2018, 19(11), 3416.
[http://dx.doi.org/10.3390/ijms19113416] [PMID: 30384456]
[54]
Zhao, X.; Chen, L.; Guo, Z-H.; Liu, T. Predicting drug side effects with compact integration of heterogeneous networks. Curr. Bioinform., 2019, 14(8), 709-720.
[http://dx.doi.org/10.2174/1574893614666190220114644]
[55]
RDKit; Open-source cheminformatics available at: . http://www.rdkit.org
[56]
Rogers, D.; Hahn, M. Extended-connectivity fingerprints. J. Chem. Inf. Model., 2010, 50(5), 742-754.
[http://dx.doi.org/10.1021/ci100050t] [PMID: 20426451]

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