Shaping the interaction landscape of bioactive molecules

Bioinformatics

Volumn 29, Issue 23, 2013, Pages 3073-3079

  Gfeller, David ^a   Michielin, Olivier ^a,b   Zoete, Vincent ^a  

^a SWISS INSTITUTE OF BIOINFORMATICS   (Switzerland)

^b LAUSANNE UNIVERSITY HOSPITAL   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

LIGAND; MOLECULAR LIBRARY; PROTEIN; PROTEIN BINDING;

BINDING SITE; BIOLOGY; CHEMISTRY; COMPUTER SIMULATION; DRUG DESIGN; FACTUAL DATABASE; HUMAN; METABOLISM; MOLECULAR LIBRARY; QUANTITATIVE STRUCTURE ACTIVITY RELATION; ARTICLE; PROTEIN BINDING;

BINDING SITES; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; DATABASES, FACTUAL; DRUG DESIGN; HUMANS; LIGANDS; PROTEIN BINDING; PROTEINS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; SMALL MOLECULE LIBRARIES;

EID: 84890023718     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btt540     Document Type: Article

References (45)

1. Armstrong, M.S. et al. (2011) Improving the accuracy of ultrafast ligand-based screening: incorporating lipophilicity into ElectroShape as an extra dimension. J. Comput. Aided Mol. Des., 25, 785-790.
2. Armstrong, M.S. et al. (2010) ElectroShape: fast molecular similarity calculations incorporating shape, chirality and electrostatics. J. Comput. Aided Mol. Des., 24, 789-801.
3. Ashburn, T.T. and Thor, K.B. (2004) Drug repositioning: identifying and developing new uses for existing drugs. Nat. Rev. Drug Discov., 3, 673-683.
4. Ballester, P.J. et al. (2009) Ultrafast shape recognition: evaluating a new ligandbased virtual screening technology. J. Mol. Graph Model, 27, 836-845.
5. Ballester, P.J. and Richards, W.G. (2007) Ultrafast shape recognition to search compound databases for similar molecular shapes. J. Comput. Chem., 28, 1711-1723.
6. Bolton, E. et al. (2008) PubChem: integrated platform of small molecules and biological activities. In: Annual Reports in Computational Chemistry. American Chemical Society, Washington, DC.
7. Campillos, M. et al. (2008) Drug target identification using side-effect similarity. Science, 321, 263-266.
8. Cleves, A.E. and Jain, A.N. (2008) Effects of inductive bias on computational evaluations of ligand-based modeling and on drug discovery. J. Comput. Aided Mol. Des., 22, 147-159.
9. Dunkel, M. et al. (2008) SuperPred: drug classification and target prediction. Nucleic Acids Res., 36, W55-W59.
10. Fontanilla, D. et al. (2009) The hallucinogen N, N-dimethyltryptamine (DMT) is an endogenous sigma-1 receptor regulator. Science, 323, 934-937.
11. Gaulton, A. et al. (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res., 40, D1100-D1107.
12. Gong, J. et al. (2013) ChemMapper: a versatile web server for exploring pharmacology and chemical structure association based on molecular 3D similarity method. Bioinformatics, 29, 1827-1829.
13. Grosdidier, A. et al. (2011) SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Res., 39, W270-W277.
14. Haupt, V.J. and Schroeder, M. (2011) Old friends in new guise: repositioning of known drugs with structural bioinformatics. Brief Bioinform., 12, 312-326.
15. Iorio, F. et al. (2010) Discovery of drug mode of action and drug repositioning from transcriptional responses. Proc. Natl Acad. Sci. USA, 107, 14621-14626.
16. Irwin, J.J. et al. (2012) ZINC: a free tool to discover chemistry for biology. J. Chem. Inf. Model, 52, 1757-1768.
17. Iskar, M. et al. (2013) Characterization of drug-induced transcriptional modules: towards drug repositioning and functional understanding. Mol. Syst. Biol., 9, 662.
18. Keiser, M.J. et al. (2007) Relating protein pharmacology by ligand chemistry. Nat. Biotechnol., 25, 197-206.
19. Keiser, M.J. et al. (2009) Predicting new molecular targets for known drugs. Nature, 462, 175-181.
20. Knox, C. et al. (2011) DrugBank 3.0: a comprehensive resource for 'omics' research on drugs. Nucleic Acids Res., 39, D1035-D1041.
21. Kola, I. and Landis, J. (2004) Can the pharmaceutical industry reduce attrition rates? Nat. Rev. Drug Discov., 3, 711-715.
22. Kramer, C. et al. (2012) The experimental uncertainty of heterogeneous public K(i) data. J. Med. Chem., 55, 5165-5173.
23. Kuhn, M. et al. (2013) Systematic identification of proteins that elicit drug side effects. Mol. Syst. Biol., 9, 663.
24. Li, H. et al. (2006) TarFisDock: a web server for identifying drug targets with docking approach. Nucleic Acids Res., 34, W219-W224.
25. Li, J. et al. (2009) Building disease-specific drug-protein connectivity maps from molecular interaction networks and PubMed abstracts. PLoS Comput. Biol., 5, e1000450.
26. Liu, X. et al. (2011) SHAFTS: a hybrid approach for 3D molecular similarity calculation. 1. Method and assessment of virtual screening. J. Chem. Inf. Model, 51, 2372-2385.
27. Lounkine, E. et al. (2012) Large-scale prediction and testing of drug activity on sideeffect targets. Nature, 486, 361-367.
28. Morris, G.M. et al. (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J. Comput. Chem., 30, 2785-2791.
29. Nettles, J.H. et al. (2006) Bridging chemical and biological space: ''target fishing'' using 2D and 3D molecular descriptors. J. Med. Chem., 49, 6802-6810.
30. Novac, N. (2013) Challenges and opportunities of drug repositioning. Trends Pharmacol. Sci., 34, 267-272.
31. Perez-Nueno, V.I. et al. (2012) Detecting drug promiscuity using Gaussian ensemble screening. J. Chem. Inf. Model, 52, 1948-1961.
32. Pollock, S.N. et al. (2008) Scaffold topologies. 1. Exhaustive enumeration up to eight rings. J. Chem. Inf. Model, 48, 1304-1310.
33. Rahman, S.A. et al. (2009) Small Molecule Subgraph Detector (SMSD) toolkit. J. Cheminform., 1, 12.
34. Renner, S. and Schneider, G. (2006) Scaffold-hopping potential of ligand-based similarity concepts. ChemMedChem, 1, 181-185.
35. Rohrer, S.G. and Baumann, K. (2008) Impact of benchmark data set topology on the validation of virtual screening methods: exploration and quantification by spatial statistics. J. Chem. Inf. Model, 48, 704-718.
36. Sastry, G.M. et al. (2011) Rapid shape-based ligand alignment and virtual screening method based on atom/feature-pair similarities and volume overlap scoring. J. Chem. Inf. Model, 51, 2455-2466.
37. Schuffenhauer, A. et al. (2007) The scaffold tree-visualization of the scaffold universe by hierarchical scaffold classification. J. Chem. Inf. Model, 47, 47-58.
38. Smith, A.M. et al. (2010) A survey of yeast genomic assays for drug and target discovery. Pharmacol. Ther., 127, 156-164.
39. Venkatraman, V. et al. (2010) Comprehensive comparison of ligand-based virtual screening tools against the DUD data set reveals limitations of current 3D methods. J. Chem. Inf. Model, 50, 2079-2093.
40. Wang, J.C. et al. (2012) idTarget: a web server for identifying protein targets of small chemical molecules with robust scoring functions and a divide-and-conquer docking approach. Nucleic Acids Res., 40, W393-W399.
41. Wang, L. et al. (2013) TargetHunter: an in silico target identification tool for predicting therapeutic potential of small organic molecules based on chemogenomic database. AAPS J, 15, 395-406.
42. Wirth, M. and Sauer, W.H. (2011) Bioactive molecules: perfectly shaped for their target. Mol. Inform., 30, 677-688.
43. Yera, E.R. et al. (2011) Chemical structural novelty: on-targets and off-targets. J. Med. Chem., 54, 6771-6785.
44. Ziegler, S. et al. (2013) Target identification for small bioactive molecules: finding the needle in the haystack. Angew. Chem. Int. Ed. Engl., 52, 2744-2792.
45. Zoete, V. et al. (2009) Docking, virtual high throughput screening and in silico fragment-based drug design. J. Cell. Mol. Med., 13, 238-248.