Chemogenomic approach to comprehensive predictions of ligand-target interactions: A comparative study

Proceedings - 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2012

Volumn , Issue , 2012, Pages 136-142

  Brown, J B ^a   Niijima, Satoshi ^a   Shiraishi, Akira ^a   Nakatsui, Masahiko ^a   Okuno, Yasushi ^a  

^a KYOTO UNIVERSITY   (Japan)

Author keywords

computational chemogenomics; kernels; ligand target interactions; machine learning

Indexed keywords

CHEMOGENOMICS; COMPARATIVE STUDIES; COMPREHENSIVE PREDICTION; COMPUTATIONAL TECHNIQUE; INTERDISCIPLINARY FIELDS; KERNELS; MACHINE LEARNING APPROACHES; PREDICTION PERFORMANCE;

BIOINFORMATICS; LEARNING SYSTEMS;

LIGANDS;

EID: 84875592278     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BIBMW.2012.6470295     Document Type: Conference Paper

References (35)

1. Klabunde' T. Chemo genomic approaches to drug discovery: similar receptors bind similar ligands. Br. J. Pharmacol. 2007' 152' 5-7
2. Bajorath' J. Computational analysis of ligand relationships within target families. Curr. Opin. Chem. Biol. 2008' 12' 352358
3. Schuffenhauer' A.; Jacoby' E. Annotating and mmmg the ligand-target chemogenomics knowledge space. Drug Discovery Today 2004' 2' 190-200
4. Rognan' D. Chemogenomic approaches to rational drug design. Br. J. Pharmacol. 2007' 152'38-52
5. Jacoby' E. Computational chemogenomics. WIREs Comput. Mol. Sci. 2011' 1' 57-67
6. Hopkins' A. L. Network pharmacology: the next paradigm in drug discovery. Nat. Chem. BioI. 2008' 4' 682-690
7. Bock' J. R.; Gough' D. A. Virtual screen for ligands of orphan G protein-coupled receptors. J. Chem. In! Model. 2005' 45' 1402-1414
8. Erhan' D.; L'Heureux' P.-J.; Yue' S. Y.; Bcngio' Y. Collaborative filtering on a family of biological targets. J. Chem. In! Model. 2006' 46' 626-635
9. Nagamine' N.; Sakakibara' Y. Statistical prediction of proteinchemical interactions based on chemical structure and mass spectrometry data. Bioinformatics 2007' 23' 2004-2012
10. Faulon' J.-L.; Misra' M.; Martin' S.; Sale' K.; Sapra' R. Genome scale enzyme-metabolite and drug-target interaction predictions using the signature molecular descriptor. Bioinformatics 2008' 24' 225-233
11. Jacob' L.; Vert' J.-P. Protein-ligand interaction prediction: an improved chemogenomics approach. Bioinformatics 2008' 24' 2149-2156
12. Strombergsson' H.; Daniluk' P.; Kryshtafovych' A.; Fidelis' K.; Wikberg' J. E.; Kleywegt' G. J.; Hvidsten' T. R. Interaction model based on local protein substructures generalizes to the entire structural enzyme-ligand space. J. Chem. In! Model. 2008' 48' 2278-88
13. Wassermann' A. M.; Geppert' H.; Bajorath' J. Ligand prediction for orphan targets using support vector machines and various target-ligand kernels is dominated by nearest neighbor effects. J. Chem. In! Model. 2009' 49' 2155-2167
14. Weill' N.; Rognan' D. Development and validation of a novel protein-ligand fingerprint to mine chemogenomic space: application to G protein-coupled receptors and their ligands. J. Chem. In! Model. 2009' 49' 1049-62
15. Yabuuchi' H.; Niijima' S.; Takematsu' H.; Ida' T.; Hirokawa' T.; Hara' T.; Ogawa' T.; Minowa' Y.; Tsujimoto' G.; Okuno' Y. Analysis of multiple compound-protein interactions reveals novel bioactive molecules. Mol. Syst. Biol. 2011' 7: 472
16. Strombergsson' H; Kleywegt' G. J. A chemogenomics view on protein-ligand spaces. BMC Bioinformatics 2009' Suppl.6' S13
17. Niijima' S.; Yabuuchi' H.; Okuno' Y. Cross-target view to feature selection: identification of molecular interaction features in ligand-target space. J. Chem. In! Model. 2011' 51' 15-24
18. Scholkopf' B.; Smola' A. J. Learning with Kernels: Support Vector Machines' Regularization' Optimization' and Beyond; MIT Press: Cambridge' MA' 2002
19. Gozalbes' R.; Pmeda-Lucena' A. Small molecule databases and chemical descriptors useful in chemoinformatics: An overview. Comb. Chem. High Throughput Screen. 2011' 14' 548-558
20. Todeschini' R.; Consonni. V. Molecular Descriptors for Chemolnfornuuics; Wiley-VCH Verlag GmbH & Co. KGaA' Weinheim' Germany' 2009
21. Durant' J. L.; Leland' B. A.; Henry' D. R.; Nourse' J. G. Reoptimization of MDL keys for use in drug discovery. J. Chem. In! Comput. Sci. 2002' 42' 1273-1280
22. Rogers' D.; Hahn' M. Extended-connectivity fingerprints. J. Chem. In! Model. 2010' 50' 742-754
23. DRAGON (Software for Molecular Descriptor Calculation) Version 6.0; Talete srl: Milano' Italy' 2011
24. Ralaivola' L.; Swamidass' S. J.; Saigo' H.; Baldi' P. Graph kernels for chemical informatics. Neural Networks 2005' 18' 1093-1110
25. Hinsclmann' G.; Rosenbaum' L.; Jahn' A.; Fechner' N.; Zell' A. jCompoundMapper: An open source Java library and command-line tool for chemical fingerprints. J. Cheminformatics 2011' 3' 3
26. MOE' version 2010.10; Chemical Computing Group Inc.: Montreal' Canada' 2010
27. Rao' H. B.; Zhu' F.; Yang' G. B.; Li' Z. R.; Chen' Y. Z. Update of PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence. Nucleic Acids Res. 2011' 39' W385W390
28. Leslie' C. S.; Eskin' E.; Cohen' A.; Weston' J.; Noble' W. S. Mismatch string kernels for discriminative protein classification. Bioinformatics 2004' 20' 467-476
29. Saigo' H.; Vert' J.-P.; Ueda' N.; Akutsu' T. Protein homology detection using string alignment kernels. Bioinformatics 2004' 20' 1682-1689
30. GVK Bio Target inhibitor databases; GVK Biosciences Private Limited: Hyderabad' India' 2007' 2011
31. AurSCOPE Ion Channels Database; Aureus Pharma: Paris' France' 2009
32. Magrane' M.; UniProt Consortium. UniProt Knowledgebase: a hub of integrated protein data. Database 2011' 2011' bar009
33. LIBSVM: a library for support vector machines; Chih-Chung Chang and Chih-Jen Lin' 2001
34. Fonseca' C.; Fleming' P. Genetic Algorithms for Multiobjective Optimization: Formulation' Discussion' and Generalization. Proc. Fifth Int. Con! Genet. Alg. 1993' 416-423
35. Fawcett' T. An introduction to ROC analysis. Pattern Recognit. Lett. 2006' 27' 861-874