Chemogenomic approaches to infer drug-target interaction networks

Methods in Molecular Biology

Volumn 939, Issue , 2013, Pages 97-113

  Yamanishi, Yoshihiro ^a  

^a INSERM U900   (France)

Author keywords

Bipartite graph; Chemical genomics; Compound protein interactions; Drug target interactions; Genomic drug discovery; Supervised network inference

Indexed keywords

EID: 84871893054     PISSN: 10643745     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-1-62703-107-3_9     Document Type: Review

References (40)

1. Wang Y, Xiao J, Suzek T, Zhang J, Wang J, Bryant S (2009) PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res 37:D623-D6333
2. Butina D, Segall M, Frankcombe K (2002) Predicting ADME properties in silico: methods and models. Drug Discov Today 7:S83-S888
3. Byvatov E, Fechner U, Sadowski J, Schneider G (2003) Comparison of support vector machine and articial neural network systems for drug/nondrug classification. J Chem Inf Comput Sci 43:1882-18899
4. Rarey M, Kramer B, Lengauer T, Klebe G (1996) A fast flexible docking method using an incremental construction algorithm. J Mol Biol 261:470-4899
5. Keiser M, Roth B, Armbruster B, Ernsberger P, Irwin J, Shoichet B (2007) Relating protein pharmacology by ligand chemistry. Nat Bio-technol 25:197-2066
6. Yildirim M, Goh K, Cusick M, Barabasi A, Vidal M (2007) Drug-target network. Nat Bio-technol 25:1119-11266
7. Zhu S, Okuno Y, Tsujimoto G, Mamitsuka H (2005) A probabilistic model for mining implicit "chemical compound-gene" relations from literature. Bioinformatics 21(Suppl 2): ii245-2511
8. Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita K, Itoh M, Kawashima S, Katayama T, Araki M, Hirakawa M (2006) From geno-mics to chemical genomics: new developments in KEGG. Nucleic Acids Res 34:D354-3577
9. Stockwell B (2000) Chemical genetics: ligand-based discovery of gene function. Nat Rev Genet 1:116-1255
10. Dobson C (2004) Chemical space and biology. Nature 432:824-8288
11. Bock JR, Gough DA (2005) Virtual screen for ligands of orphan G protein-coupled receptors. J Chem Inf Model 45:1402-14144
12. Erhan D, Lheureux P-J, Yue SY, Bengio Y (2006) Collaborative ltering on a family of biological targets. J Chem Inf Model 46:626-6355
13. Nagamine N, Sakakibara Y (2007) Statistical prediction of proteinchemical interactions based on chemical structure and mass spectrometry data. Bioinformatics 23:2004-20122
14. Faulon J, Misra M, Martin S, Sale K, Sapra R (2008) Genome scale enzyme-metabolite and drug-target interaction predictions using the signature molecular descriptor. Bioinformatics 24:225-2333
15. Jacob L, Vert J-P (2008) Protein-ligand interaction prediction: an improved chemoge-nomics approach. Bioinformatics 24:2149-21566
16. Bleakley K, Yamanishi Y (2009) Supervised prediction of drug-target interactions using bipartite local models. Bioinformatics 25:2397-24033
17. 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. Bioinformat-ics 24:i232-i2400
18. Yamanishi Y (2009) Supervised bipartite graph inference. In Koller D, Schuurmans D, Bengio Y, Bottou L (eds) Advances in Neural Information Processing Systems, vol 21. MIT, Cambridge, MA, pp 1841-18488
19. Campillos M, Kuhn M, Gavin A, Jensen L, Bork P (2008) Drug target identification using side-effect similarity. Science 321 (5886):263-2666
20. Yamanishi Y, Kotera M, Kanehisa M, Goto S (2010) Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework. Bioinformat-ics 26:i246-i2544
21. Atias N, Sharan R (2011) An algorithmic framework for predicting side-effects of drugs. Journal of Computational Biology, 18, 207-2188
22. Kanehisa M, Araki M, Goto S, Hattori M, Hir-akawa M, Itoh M, Katayama T, Kawashima S, Okuda S, Tokimatsu T, Yamanishi Y (2008) KEGG for linking genomes to life and the environment. Nucleic Acids Res 36(Database issue):D480-D4855
23. Gunther S, Guenther S, Kuhn M, Dunkel M et al (2008) SuperTarget and Matador: resources for exploring drug-target relationships. Nucleic Acids Res 36(Database issue): D919-D9222
24. Wishart D, Knox C, Guo A, Cheng D, Shrivas-tava S, Tzur D, Gautam B, Hassanali M (2008) DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res 36(Database issue): D901-D9066
25. Hattori M, Okuno Y, Goto S, Kanehisa M (2003) Development of a chemical structure comparison method for integrated analysis of chemical and genomic information in the metabolic pathways. J Am Chem Soc 125:11853-118655
26. Smith T, Waterman M (1981) Identification of common molecular subsequences. J Mol Biol 147:195-1977
27. Schölkopf B, Tsuda K, Vert J (2004) Kernel methods in computational biology. MIT, Cambridge, MAA
28. Lodhi H, Yamanishi Y (2010) Chemoinfor-matics and advanced machine learning perspectives: complex computational methods and collaborative techniques. IGI Global, USAA
29. Todeschini R, Consonni V (2002) Handbook of molecular descriptors. Wiley, New Yorkk
30. Kondor R, Lafferty J (2002) Diffusion kernels on graphs and other discrete input spaces. In: Faucett T, Mishra N (eds) Proceedings of the twentieth international conference on machine learning. AAAI Press, USA, pp 321-3288
31. Scholkopf B, Smola A, Muller K-R (1998) Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput 10:1299-13199
32. Wahba G (1990) Splines models for observational data: series in applied mathematics. SIAM, Philadelphiaa
33. Girosi F, Jones M, Poggio T (1995) Regulari-zation theory and neural networks architectures. Neural Computation 7:219-2699
34. Shawe-Taylor J, Cristianini N (2004) Kernel methods for pattern analysis. Cambridge University Press, Cambridgee
35. Bleakley K, Biau G, Vert J-P (2007) Supervised reconstruction of biological networks with local models. Bioinformatics 23:i57-i655
36. Joachims T (2003) Learning to classify text using support vector machines: methods, theory and algorithms. Kluwer Academic, Dordrechtt
37. Ishibashi K, Hatano K, Takeda M (2008) Online learning of approximate maximum p-norm margin classiers with biases. Proceedings of the 21st annual conference on learning theory (COLT2008), 69-800
38. Mahe P, Ralaivola L, Stoven V, Vert J (2006) The pharmacophore kernel for virtual screening with support vector machines. J Chem Inf Model 46:2003-20144
39. Kratochwil N, Malherbe P, Lindemann L, Ebel-ing M, Hoener M, Muhlemann A, Porter R, Stahl M, Gerber P (2005) An automated system for the analysis of G protein-coupled receptor transmembrane binding pockets: alignment, receptor-based pharmacophores, and their application. J Chem Inf Model 45:1324-13366
40. Jacob L, Hoffmann B, Stoven V, Vert J-P (2008) Virtual screening of gpcrs: an in silico chemogenomics approach. BMC Bioinformatics 9:3633