Novel method for pharmacophore analysis by examining the Joint Pharmacophore Space

Journal of Chemical Information and Modeling

Volumn 51, Issue 5, 2011, Pages 1106-1121

  Ranu, Sayan ^a   Singh, Ambuj K ^a  

^a University of California   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CHEMICAL ANALYSIS; CLASSIFICATION (OF INFORMATION); GEOMETRY; QUERY PROCESSING;

BLOOD-BRAIN BARRIER; CLASSIFICATION QUALITY; GEOMETRIC CONFIGURATIONS; JOINT SPACE; MOLECULAR DESCRIPTORS; PHARMACOPHORES; POSITIVE/NEGATIVE;

PHARMACODYNAMICS;

ANTINEOPLASTIC AGENT; LIGAND;

ALGORITHM; ARTICLE; BINDING SITE; BLOOD BRAIN BARRIER; CAPILLARY PERMEABILITY; CHEMICAL DATABASE; CHEMICAL PHENOMENA; CHEMISTRY; CONFORMATION; DATA MINING; DRUG DEVELOPMENT; HUMAN; HYDROGEN BOND; MOLECULARLY TARGETED THERAPY; TUMOR CELL LINE;

ALGORITHMS; ANTINEOPLASTIC AGENTS; BINDING SITES; BLOOD-BRAIN BARRIER; CAPILLARY PERMEABILITY; CELL LINE, TUMOR; DATA MINING; DATABASES, CHEMICAL; DRUG DISCOVERY; HUMANS; HYDROGEN BONDING; HYDROPHOBIC AND HYDROPHILIC INTERACTIONS; LIGANDS; MOLECULAR CONFORMATION; MOLECULAR TARGETED THERAPY;

EID: 79960267520     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci100503y     Document Type: Article

References (57)

1. Sams-Dodd, F. Target-based drug discovery: is something wrong? Drug Discovery Today 2005, 10 139-147. (Pubitemid 40247812)
2. Jenkins, J. L.; Bender, A.; Davies, J. W. In silico target fishing: Predicting biological targets from chemical structure. Drug Discovery Today: Technol. 2006, 3, 413-421. (Pubitemid 46158395)
3. Rishton, G. M. Reactive compounds and in vitro false positives in HTS. Drug Discovery Today 1997, 2, 382-384. (Pubitemid 27368216)
4. Yamanishi, Y.; Araki, M.; Gutteridge, A.; Honda, W.; Kanehisa, M. Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics 2008, 24, 232-240.
5. Keiser, M. J.; Roth, B. L.; Armbruster, B. N.; Ernsberger, P.; Irwin, J. J.; Shoichet, B. K. Relating protein pharmacology by ligand chemistry. Nat. Biotechnol. 2007, 25, 197-206. (Pubitemid 46227119)
6. Yildirim, M. A.; Goh, K. I.; Cusick, M. E.; Barabasi, A. L.; Vidal, M. Drug-target network. Nat. Biotechnol. 2007, 25, 1119-1126. (Pubitemid 47538114)
7. Cheng, A. C.; Coleman, R. G.; Smyth, K. T.; Cao, Q.; Soulard, P.; Caffrey, D. R.; Salzberg, A. C.; Huang, E. S. Structure-based maximal affinity model predicts smallmolecule druggability. Nat. Biotechnol 2007, 25, 71-75. (Pubitemid 46087905)
8. Rarey, M.; Kramer, B.; Lengauer, T.; Klebe, G. A fast flexible docking method using an incremental construction algorithm. J. Mol. Biol. 1996, 261 470-489. (Pubitemid 26335901)
9. Wale, N.; Karypis, G. Target Fishing for Chemical Compounds Using Target-Ligand Activity Data and Ranking Based Methods. J. Chem. Inf. Model. 2009, 49, 2190-2201.
10. Nettles, J. H.; Jenkins, J. L.; Bender, A.; Deng, Z.; Davies, J. W.; Glick, M. Bridging chemical and biological space: "target fishing" using 2D and 3D molecular descriptors. J. Med. Chem. 2006, 49, 6802-6810. (Pubitemid 44749747)
11. Nidhi,; Glick, M.; Davies, J. W.; Jenkins, J. L. Prediction of Biological Targets for Compounds Using Multiple-Category Bayesian Models Trained on Chemogenomics Databases. J. Chem. Inf. Model 2006, 46 1124-1133. (Pubitemid 43999157)
12. Niwa, T. Prediction of biological targets using probabilistic neural networks and atom-type descriptors. J. Med. Chem. 2004, 47 2645-2650. (Pubitemid 38580100)
13. Warmuth, M. K.; Liao, J.; RAd'tsch, G.; Mathieson, M.; Putta, S.; Lemmen, C. Active learning with support vector machines in the drug discovery process. J. Chem. Inf. Comput. Sci. 2003, 43, 2003.
14. Klabunde, T. Chemogenomic approaches to drug discovery: similar receptors bind similar ligands. Br. J. Pharmacol. 2007, 152, 5-7. (Pubitemid 47339899)
15. Jacob, L.; Vert, J. P. Protein-ligand interaction prediction: an improved chemogenomics approach. Bioinformatics 2008, 24, 2149-2156.
16. Bock, J. R.; Gough, D. A. Virtual Screen for Ligands of Orphan G. Protein-Coupled Receptors. J. Chem. Inf. Model 2005, 45, 1402-1414. (Pubitemid 41476023)
17. Deng, Z.; Chuaqui, C.; Singh, J. Structural Interaction Fingerprint (SIFt):A Novel Method for Analyzing Three-Dimensional Protein-Ligand Binding Interactions. J. Med. Chem. 2004, 47, 337-344. (Pubitemid 38057878)
18. Erhan, D.; L'Heureux, P.-J.; Yue, S. Y.; Bengio, Y. Collaborative Filtering on a Family of Biological Targets. J. Chem. Inf. Model 2006, 46 626-635.
19. Geppert, H.; Humrich, J.; Stumpfe, D.; Gartner, T.; Bajorath, J. Ligand Prediction from Protein Sequence and Small Molecule Information Using Support Vector Machines and Fingerprint Descriptors. J. Chem. Inf. Model 2009, 49, 767-779.
20. Lapinsh, M.; Prusis, P.; Uhlen, S.; Wikberg, J. E. Improved approach for proteochemometrics modeling: application to organic compound - amine G. protein-coupled receptor interactions. Bioinformatics 2005, 21 4289-4296. (Pubitemid 41724309)
21. Lindstrom, A.; Pettersson, F.; Almqvist, F.; Berglund, A.; Kihlberg, J.; Linusson, A. Hierarchical PLS Modeling for Predicting the Binding of a Comprehensive Set of Structurally Diverse Protein-Ligand Complexes. J. Chem. Inf. Model. 2006, 46 1154-1167. (Pubitemid 43999160)
22. Ning, X.; Rangwala, H.; Karypis, G. Multi-Assay-Based Structure-Activity Relationship Models: Improving Structure-Activity Relationship Models by Incorporating Activity Information from Related Targets. J. Chem. Inf. Model. 2009, 49, 2444-2456.
23. Strombergsson, H.; Daniluk, P.; Kryshtafovych, A.; Fidelis, K.; Wikberg, J. E. S.; Kleywegt, G. J.; Hvidsten, T. R. Interaction Model Based on Local Protein Substructures Generalizes to the Entire Structural Enzyme-Ligand Space. J. Chem. Inf. Model 2008, 48, 2278-2288.
24. 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. Inf. Model 2009, 49, 1049-1062.
25. Zhao, Y. H.; Abraham, M. H.; Ibrahim, A.; Fish, P. V.; Cole, S.; Lewis, M. L.; de Groot, M. J.; Reynolds, D. P. Predicting penetration across the blood-brain barrier from simple descriptors and fragmentation schemes. J. Chem. Inf. Model. 2007, 47, 170-175. (Pubitemid 46225573)
26. Dixon, S. L.; Smondyrev, A. M.; Rao, S. N. PHASE: a novel approach to pharmacophore modeling and 3D database searching. Chem. Biol. Drug Des. 2006, 67 370-372.
27. Dixon, S. L.; Smondyrev, A. M.; Knoll, E. H.; Rao, S. N; Shaw, D. E.; Friesner, R. A. PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. J. Comput.-Aided Mol Des. 2006, 20 647-671. (Pubitemid 46023928)
28. Kurogi, Y.; Guner, O. F. Pharmacophore modeling and three-dimensional database searching for drug design using catalyst. Curr. Med. Chem. 2001, 8, 1035-1055. (Pubitemid 32633658)
29. Guner, O.; Clement, O.; Kurogi, Y. Pharmacophore modeling and three dimensional database searching for drug design using catalyst: recent advances. Curr. Med. Chem. 2004, 11 2991-3005. (Pubitemid 39369268)
30. Wolber, G.; Langer, T. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J. Chem. Inf. Model. 2005, 45, 160-169. (Pubitemid 40736968)
31. Molecular Operating Environment (MOE). http://www.chemcomp.com/index.htm (accessed April 9, 2011).
32. Diller, D.J. In Silico hERG Modeling: Challenges and Progress. Curr. Comput.-Aided Drug Des. 2009, 5, 106-121.
33. Daylight Theory Manual; Daylight Chemical Information Systems Inc.: Aliso Viejo, CA, 2008.
34. Saeh, J. C.; Lyne, P. D.; Takasaki, B. K.; Cosgrove, D. A. Lead Hopping Using SVM and 3D Pharmacophore Fingerprints. J. Chem. Inf. Model. 2005, 45, 1122-1133. (Pubitemid 41161731)
35. McGregor, M. J.; Muskal, S. M. Pharmacophore Fingerprinting. 1. Application to QSAR and Focused Library Design. J. Chem. Inf. Comput. Sci. 1999, 39 569-574. (Pubitemid 129539215)
36. Beno, B. R.; Mason, J. S. The design of combinatorial libraries using properties and 3D pharmacophore fingerprints. Drug Discovery Today 2001, 6 251-258.
37. van Dongen, S. Ph.D. Thesis, University of Utrecht, 2000.
38. Kabsch, W. A solution of the best rotation to relate two sets of vectors. Acta Crystallogr. 1976, 922.
39. Wolfram MathWorld. http://mathworld.wolfram.com/BinomialDistribution.html (accessed November 16, 2010).
40. Vapnik, V. Statistical Learning Theory; John Wiley: New York, NY, USA
41. Swamidass, S. J.; Chen, J.; Bruand, J.; Phung, P.; Ralaivola, L.; Baldi, P. Kernels for small molecules and the prediction ofmutagenicity, toxicity and anti-cancer activity. Bioinformatics 2005, 21 359-368.
42. Bajorath, J. Integration of virtual and high-throughput screening. Nat. Rev. Drug. Discovery 2002, 1 882-894. (Pubitemid 37361583)
43. Bohm, H.-J.; Schneider, G. Virtual Screening for Bioactive Molecules; John Wiley & Sons, Inc.: New York, NY, USA, 2000.
44. Whittle, M.; Gillet, V. J.; Willett, P.; Alex, A.; Loesel, J. Enhancing the effectiveness of virtual screening by fusing nearest neighbor lists: a comparison of similarity coefficients. J. Chem. Inf. Comput. Sci. 2004, 44, 1840-1848.
45. Ranu, S.; Singh, A. K. Mining Statistically Significant Molecular Substructures for Efficient Molecular Classification. J. Chem. Inf. Model 2009, 49, 2537-2550.
46. Pubchem. (http://pubchem.ncbi.nlm.nih.gov (accessed November 16, 2010).
47. Huang, N.; Shoichet, B. K.; Irwin, J. J. Benchmarking Sets for Molecular Docking. J. Med. Chem. 2006, 49, 6789-6801. (Pubitemid 44749746)
48. JOELib: a java based computational chemistry package. http://joelib.sourceforge.net/ (accessed November 16, 2010).
49. Ketchen, D. J.; Shook, C. L. The application of cluster analysis in strategic management research: an analysis and critique. Strategic Management J. 1996, 17 441-458. (Pubitemid 126482200)
50. Yan, M. Ph.D. Thesis, Virginia Polytechnic Institute and State University, 2005.
51. Bender, A.; Mussa, H. Y.; Glen, R. C.; Reiling, S. Similarity searching of chemical databases using atom environment descriptors (MOLPRINT 2D): evaluation of performance. J. Chem. Inf. Comput. Sci. 2004, 44, 1708-1718.
52. Chang, C.-C.; Lin, C.-J. LIBSVM: a library for support vector machines; 2009. http://www.csie.ntu.edu.tw/cjlin/libsvm (accessed April 29, 2009).
53. Truchon, J.-F. F.; Bayly, C. I. Evaluating Virtual Screening Methods: Good and Bad Metrics for the "Early Recognition" Problem. J. Chem. Inf. Model 2007, 47, 488-508. (Pubitemid 46615951)
54. Swamidass, S. J.; Azencott, C.-A.; Daily, K.; Baldi, P. A CROC stronger than ROC: measuring, visualizing and optimizing early retrieval. Bioinformatics 2010, 26 1348-1356.
55. Zhao, W.; Hevener, K.; White, S.; Lee, R.; Boyett, J. A statistical framework to evaluate virtual screening. BMC Bioinformatics 2009, 10 225.
56. Clark, R.; Webster-Clark, D. Managing bias in ROC curves. J. Comput.-Aided Mol. Des. 2008, 22, 141-146.
57. Langer, T. Pharmacophores and Pharmacophore Searches; John-Wiley and Sons: Chichester, 2006.