Abstract
Drug-target networks have aided in many target prediction studies aiming at drug repurposing or the analysis of side effects. Conventional drug-target networks are bipartite. They contain two different types of nodes representing drugs and targets, respectively, and edges indicating pairwise drug-target interactions. In this work, we introduce a tripartite network consisting of drugs, other bioactive compounds, and targets from different sources. On the basis of analog relationships captured in the network and so-called neighbor targets of drugs, new drug targets can be inferred. The tripartite network was found to have a stable structure and simulated network growth was accompanied by a steady increase in assortativity, reflecting increasing correlation between degrees of connected nodes leading to even network connectivity. Local drug environments in the tripartite network typically contained neighbor targets and revealed interesting drug-compound-target relationships for further analysis. Candidate targets were prioritized. The tripartite network design extends standard drug-target networks and provides additional opportunities for drug target prediction.
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Paolini GV, Shapland RH, van Hoorn WP, Mason JS, Hopkins AL (2006) Global mapping of pharmacological space. Nat Biotechnol 24:805–815
Boran AD, Iyengar R (2010) Systems approaches to polypharmacology and drug discovery. Curr Opin Drug Discov Devel 13:297–309
Rask-Andersen M, Almén MS, Schiöth HB (2011) Trends in the exploitation of novel drug targets. Nat Rev Drug Discov 10:579–590
Bleakley K, Yamanishi Y (2009) Supervised prediction of drug–target interactions using bipartite local models. Bioinformatics 25:2397–2403
Yildirim MA, Goh KI, Cusick ME, Barabási AL, Vidal M (2007) Drug-target network. Nat Biotechnol 25:1119–1126
Campillos M, Kuhn M, Gavin AC, Jensen LJ, Bork P (2008) Drug target identification using side-effect similarity. Science 321:263–266
Ashburn TT, Thor KB (2004) Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov 3:673–683
Liu Z, Fang H, Reagan K, Xu X, Mendrick DL, Slikker W, Tong W (2013) In silico drug repositioning—what we need to know. Drug Discov Today 18:110–115
Keiser MJ, Roth BL, Armbruster BN, Ernsberger P, Irwin JJ, Shoichet BK (2007) Relating protein pharmacology by ligand chemistry. Nat Biotechnol 25:197–206
Hu Y, Lounkine E, Bajorath J (2014) Many approved drugs have bioactive analogs with different target annotations. AAPS J 16:847–859
Yamanishi Y, Araki M, Gutteridge A, Honda W, Kanehisa M (2008) Prediction of drug–target interaction networks from the integration of chemical and genomic spaces. Bioinformatics 24:i232-i240
Lapinsh M, Prusis P, Uhlén S, Wikberg JE (2005) Improved approach for proteochemometrics modeling: application to organic compound—amine G protein coupled receptor interactions. Bioinformatics 21:4289–4296
Jacob L, Vert JP (2008) Protein-ligand interaction prediction: an improved chemogenomics approach. Bioinformatics 24:2149–2156
Cheng F, Liu C, Jiang J, Lu W, Li W, Liu G, Zhou W, Huang J, Tang Y (2012) Prediction of drug-target interactions and drug repositioning via network-based inference. PLoS Comput Biol 8:e1002503
Alaimo S, Pulvirenti A, Giugno R, Ferro A (2013) Drug–target interaction prediction through domain-tuned network-based inference. Bioinformatics 29:2004–2008
Emig D, Ivliev A, Pustovalova O, Lancashire L, Bureeva S, Nikolsky Y, Bessarabova M (2013) Drug target prediction and repositioning using an integrated network-based approach. PLoS ONE 8:e60618
van Laarhoven T, Nabuurs SB, Marchiori E (2011) Gaussian interaction profile kernels for predicting drug–target interaction. Bioinformatics 27:3036–3043
Mei JP, Kwoh CK, Yang P, Li XL, Zheng J (2012) Drug–target interaction prediction by learning from local information and neighbors. Bioinformatics 29:238–245
Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, Sajed T, Johnson D, Li C, Sayeeda Z, Assempour N, Iynkkaran I, Liu Y, Maciejewski A, Gale N, Wilson A, Chin L, Cummings R, Le D, Pon A, Knox C, Wilson M (2017) DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. https://doi.org/10.1093/nar/gkx1037
Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B, Overington JP (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40(database issue):D1100–D1107
Kenny PW, Sadowski J (2004) Chemoinformatics in drug discovery. In: Oprea TI (ed) Structure modification in chemical databases. Wiley, Weinheim, pp 271–285
Hussain J, Rea C (2010) Computationally efficient algorithm to identify matched molecular pairs (MMPs) in large data sets (2010). J Chem Inf Model 50:339–348
Lewell XQ, Judd DB, Watson SP, Hann MM (1998) RECAP–retrosynthetic combinatorial analysis procedure: a powerful new technique for identifying privileged molecular fragments with useful applications in combinatorial chemistry. J Chem Inf Comput Sci 38:511–522
de la Vega de León A, Bajorath J (2014) Matched molecular pairs derived by retrosynthetic fragmentation. Med Chem Commun 5:64–67
OEChem TK version 2.0.0; OpenEye Scientific Software. Santa Fe, NM
Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T (2010) Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27:431–432
Newman M (2010) Networks—an introduction, Oxford University Press Inc., New York
Csardi G, Nepusz T (2006) The iGraph software package for complex network research. InterJ Complex Sys 1695:1–9
Maggiora GM, Shanmugasundaram V (2004) Molecular similarity measures. In: Bajorath J (ed) Chemoinformatics—concepts, methods, and tools for drug discovery. Humana Press, Totowa
Maggiora GM, Vogt M, Stumpfe D, Bajorath J (2014) Molecular similarity in medicinal chemistry. J Med Chem 57:3186–3204
Wang L, Bao SH, Pan PP, Xia MM, Chen MC, Liang BQ, Dai DP, Cai JP, Hu GX (2015) Effect of CYP2C9 genetic polymorphism on the metabolism of flurbiprofen in vitro. Drug Dev Ind Pharm 41:1363–1367
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We thank the OpenEye Scientific Software, Inc., for providing a free academic license of the OpenEye chemistry toolkit.
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Kunimoto, R., Bajorath, J. Design of a tripartite network for the prediction of drug targets. J Comput Aided Mol Des 32, 321–330 (2018). https://doi.org/10.1007/s10822-018-0098-x
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DOI: https://doi.org/10.1007/s10822-018-0098-x