Predicting drug-target interactions using drug-drug interactions

PLoS ONE

Volumn 8, Issue 11, 2013, Pages

  Kim, Shinhyuk ^a   Jin, Daeyong ^a   Lee, Hyunju ^a  

^a Gwangju Institute of Science and Technology (GIST)   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

DATABASES, CHEMICAL; DRUG INTERACTIONS; PHARMACOLOGY; SUPPORT VECTOR MACHINES;

EID: 84894270548     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0080129     Document Type: Article

References (37)

1. Knox C, Law V, Jewison T, Liu P, Ly S, et al. (2011) DrugBank 3.0: a comprehensive resource for 'omics' research on drugs. Nucleic Acids Res 39: D1035-41.
2. Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M (2010) KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res 38: D355-60.
3. Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, et al. (2011) Chembl: a large-scale bioactivity database for drug discovery. Nucleic Acids Research : 1-8.
4. Kuhn M, Szklarczyk D, Franceschini A, Campillos M, von Mering C, et al. (2010) Stitch 2: an interaction network database for small molecules and proteins. Nucleic Acids Res 38: D552-D556.
5. Rarey M, Kramer B, Lengauer T, Klebe G (1996) A fast flexible docking method using an incremental construction algorithm. J Mol Biol 261: 470-489.
6. Zhu S, Okuno Y, Tsujimoto G, Mamitsuka H (2005) A probabilistic model for mining implicit chemical compound-gene relations from literature. Bioinformatics 21: ii245-ii251.
7. Ferrara P, Jacoby E (2007) Evaluation of the utility of homology models in high throughput docking. J Mol Model 13: 897-905.
8. 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.
9. Cheng AC, Coleman RG, Smyth KT, Cao Q, Soulard P, et al. (2007) Structure-based maximal affinity model predicts small-molecule druggability. Nat Biotechnology 25: 71-75.
10. Johnson MA, Maggiora GM (1990) Concepts and applications of molecular similarity. New York: John Wiley, 99-117 pp.
11. Matter H (1997) Selecting optimally diverse compounds from structure databases: A validation study of two-dimensional and three-dimensional molecular descriptors. J Med Chem 40: 1219-1229.
12. Whittle M, Gillet VJ, Willett P, Alex A, Loesel J (2004) Enhancing the effectiveness of virtual screening by fusing nearest neighbor lists: A comparison of similarity coefficients. J Chem Inf Comput Sci 44: 1840-1848.
13. Keiser MJ, Roth BL, Armbruster BN, Ernsberger P, Irwin JJ, et al. (2007) Relating protein pharmacology by ligand chemistry. Nat Biotechnology 25: 197-206.
14. Bleakley K, Yamanishi Y (2009) Supervised prediction of drug-target interactions using bipartite local models. Bioinformatics 25: 2397-2403.
15. Jacob L, Vert JP (2008) Protein-ligand interaction prediction: an improved chemogenomics approach. Bioinformatics 24: 2149-2156.
16. Faulon JL, Misra M, Martin S, Sale K, Sapra R (2008) Genome scale enzyme-metabolite and drug-target interaction predictions using the signature molecular descriptor. Bioinformatics 24: 225-233.
17. Campillos M, Kuhn M, Gavin AC, Jensen LJ, Bork P (2008) Drug target identification using side-effect similarity. Science 321: 263-266.
18. Yamanishi Y, Kotera M, Kanehisa M, Goto S (2010) Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework. Bioinformatics 26: i246-i254.
19. Lanckriet GRG, Deng M, Cristianini N, Jordan MI, Noble WS (2004) Kernelbased data fusion and its application to protein function prediction in yeast. Pacific Symposium on Biocomputing 9: 300-311.
20. Lanckriet GRG, De Bie T, Cristianini N, Jordan MI, Noble WS (2004) A statistical framework for genomic data fusion. Bioinformatics 20: 2626-2635.
21. Ko S, Lee H (2009) Integrative approaches to the prediction of protein functions based on the feature selection. BMC Bioinformatics 10: 455.
22. Bjornsson TD, Callaghan JT, Einolf HJ, Fischer V, Gan L, et al. (2003) The conduct of in vitro and in vivo drug-drug interaction studies: A phrma perspective. J Clinical Pharmacology 43: 443-469.
23. Drugscom (2012). Drug information online. http://www.drugs.com (accessed June 25, 2012).
24. Kuhn M, Campillos M, Letunic I, Jensen LJ, Bork P (2010) A side effect resource to capture phenotypic effects of drugs. Mol Syst Biol 6: 343.
25. Rahman S, Bashton M, Holliday G, Schrader R, Thornton J (2009) Small molecule subgraph detector (smsd) toolkit. J Cheminf 1: 12.
26. Faulon JL (2012). Signature molecular descriptor. http://www.issb. genopole.fr/faulon/sscan.php (accessed June 25, 2012).
27. Gerstein M, Sonnhammer EL, Chothia C (1994) Volume changes in protein evolution. J Mol Biol 236: 1067-1078.
28. Lee H, Tu Z, Deng M, Sun F, Chen T (2006) Diffusion kernel-based logistic regression models for protein function prediction. OMICS: A Journal of Integrative Biology 10: 40-55.
29. Tibshirani R (1996) Regression shrinkage and selection via the lasso. J Royal Statist Soc B 58: 267-288.
30. Koh K, Kim SJ, Boyd S (2007) An interior-point method for large-scale l1-regularized logistic regression. J Mach Learn Res 8: 1519-1555.
31. Vapnik V (1998) Statistical Learning Theory. New York: Wiley-Interscience.
32. Obozinski G (2012). Sequential minimal optimization for multiple kernel learning. http://www.di.ens.fr/obozinski/SKMsmo.tar (accessed June 25, 2012).
33. Smith T, Waterman M (1981) Identification of common molecular subsequences. J Mol Biol 147: 195-197.
34. Wang Y, Zhang C, Deng N, Wang Y (2011) Kernel-based data fusion improves the drug-protein interaction prediction. Comput Biol Chem 35: 353-362.
35. Mattingly CJ, Colby GT, Forrest JN, Boyer JLa (2003) The comparative toxicogenomics database (ctd). Environmental health perspectives 6: 793-795.
36. Liu T, Lin Y, Wen X, Jorissen RN, Gilson MK (2007) Bindingdb: a webaccessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res 35: D198-D201.
37. Bach FR, Lanckriet GRG, Jordan MI (2004). Multiple kernel learning, conic duality, and the smo algorithm. In Proceedings of the twenty-first international conference on Machine learning (ICML 2004), Banff, Alberta, Canada, July 4-8, 2004; Brodley, Carla E., Ed.; ACM: New York, USA.