Pharmacophore Modeling and Pharmacophore-Based Virtual Screening

In Silico Drug Discovery and Design: Theory, Methods, Challenges, and Applications

Volumn , Issue , 2015, Pages 123-154

  Akram, Muhammad ^a   Kaserer, Teresa ^a   Schuster, Daniela ^a  

^a UNIVERSITY OF INNSBRUCK   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85020604893     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1201/b18799-10     Document Type: Chapter

References (103)

1. Babaoglu, K., A. Simeonov, J. J. Irwin et al. 2008. Comprehensive mechanistic analysis of hits from high-throughput and docking screens against beta-lactamase. J. Med. Chem. 51(8): 2502–2511.
2. Bajorath, J. 2002. Integration of virtual and high-throughput screening. Nat. Rev. Drug. Discov. 1(11): 882–894.
3. Barnum, D., J. Greene, A. Smellie, and P. Sprague. 1996. Identification of common functional configurations among molecules. J. Chem. Inf. Comput. Sci. 36(3): 563–571.
4. Beckett, A. H., N. J. Harper, and J. W. Clitherow. 1963. The importance of stereoisomerism in muscarinic activity. J. Pharm. Pharmacol. 15(1): 362–371.
5. Berman, H. M., J. Westbrook, Z. Feng et al. 2000. The protein data bank. Nucl. Acids Res. 28(1): 235–242.
6. Böhm, H. J. 1992. The computer program LUDI: A new method for the de novo design of enzyme inhibitors. J. Comput. Aided Mol. Des. 6(1): 61–78.
7. Bolton, E. E., Y. Wang, P. A. Thiessen, and S. H. Bryant. 2008. PubChem: Integrated platform of small molecules and biological activities. In: Annual Reports in Computational Chemistry, A. W. Ralph and C. S. David (eds.), Oxford, UK; Amsterdam, The Netherlands: Elsevier, pp. 217–241.
8. Braga, R. C. and C. H. Andrade. 2013. Assessing the performance of 3D pharmacophore models in virtual screening: How good are they. Curr. Top. Med. Chem. 13(9): 1127–1138.
9. Chen, J. and L. Lai. 2006. Pocket v.2: Further developments on receptor-based pharmacophore modeling. J. Chem. Inf. Model. 46(6): 2684–2691.
10. Chen, X., Y. Lin, and M. K. Gilson. 2001. The binding database: Overview and user’s guide. Biopolymers 61(2): 127–141.
11. Chong, C. R., X. Chen, L. Shi, J. O. Liu, and D. J. Sullivan, Jr. 2006. A clinical drug library screen identifies astemizole as an antimalarial agent. Nat. Chem. Biol. 2(8): 415–416.
12. Cottrell, S. J., V. J. Gillet, and R. Taylor. 2006. Incorporating partial matches within multiobjective pharmacophore identification. J. Comput. Aided Mol. Des. 20: 735–749.
13. Cross, S., M. Baroni, L. Goracci, and G. Cruciani. 2012. GRID-based three-dimensional pharmacophores I: FLAPpharm, a novel approach for pharmacophore elucidation. J. Chem. Inf. Model. 52(10): 2587–2598.
14. Dixon, S. L., A. M. Smondyrev, E. H. Knoll, S. N. Rao, D. E. Shaw, and R. A. Friesner. 2006. 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. 20(10–11): 647–671.
15. Doman, T. N., S. L. McGovern, B. J. Witherbee et al. 2002. Molecular docking and high-throughput screening for novel inhibitors of protein tyrosine phosphatase- 1B. J. Med. Chem. 45(11): 2213–2221.
16. Dror, O., D. Schneidman-Duhovny, Y. Inbar, R. Nussinov, and H. J. Wolfson. 2009. Novel approach for efficient pharmacophore-based virtual screening: Method and applications. J. Chem. Inf. Model. 49(10): 2333–2343.
17. Duffy, B. C., L. Zhu, H. Decornez, and D. B. Kitchen. 2012. Early phase drug discovery: Cheminformatics and computational techniques in identifying lead series. Bioorg. Med. Chem. 20(18): 5324–5342.
18. Durrant, J. D. and J. A. McCammon. 2011. Molecular dynamics simulations and drug discovery. BMC Biol. 9: 71–80.
19. Ehrlich, P. 1909. Über den jetzigen Stand der Chemotherapie. Ber. Dtsch. Chem. Ges. 42(1): 17–47.
20. Ekins, S., A. J. Williams, M. D. Krasowski, and J. S. Freundlich. 2011. In silico repositioning of approved drugs for rare and neglected diseases. Drug Discov. Today 16(7–8): 298–310.
21. Ertl, P. 2014. Intuitive ordering of scaffolds and scaffold similarity searching using scaffold keys. J. Chem. Inf. Model. 54(6): 1617–1622.
22. Ferreira, R. S., A. Simeonov, A. Jadhav et al. 2010. Complementarity between a docking and a high-throughput screen in discovering new cruzain inhibitors. J. Med. Chem. 53(13): 4891–4905.
23. Ganter, B., R. D. Snyder, D. N. Halbert, and M. D. Lee. 2006. Toxicogenomics in drug discovery and development: Mechanistic analysis of compound/class-dependent effects using the DrugMatrix® database. Pharmacogenomics 7(7):1025–1044.
24. Gao, H., C. Williams, P. Labute, and J. Bajorath. 1999. Binary quantitative structure– activity relationship (QSAR) analysis of estrogen receptor ligands. J. Chem. Inf. Comput. Sci. 39(1): 164–168.
25. Garcia-Quiroz, J. and J. Camacho. 2011. Astemizole: An old anti-histamine as a new promising anti-cancer drug. Anticancer Agents Med. Chem. 11(3): 307–314.
26. García-Quiroz, J., R. García-Becerra, D. Barrera et al. 2012. Astemizole synergizes calcitriol antiproliferative activity by inhibiting CYP24A1 and upregulating VDR: A novel approach for breast cancer therapy. PLoS ONE 7(9): e45063.
27. Gaulton, A., L. J. Bellis, A. P. Bento et al. 2012. ChEMBL: A large-scale bioactivity database for drug discovery. Nucleic Acids Res. 40(Database issue): D1100– D1107.
28. Greene, J., S. Kahn, H. Savoj, P. Sprague, and S. Teig. 1994. Chemical function queries for 3D database search. J. Chem. Inf. Comput. Sci. 34(6): 1297–1308.
29. Güner, F. and R. Henry. 2000. Metric for analyzing hit-lists and pharmacophores. In: Pharmacophore Perception, Development, and Use in Drug Design, F. G. Osman (ed.), La Jolla, CA: International University Line, pp. 193–12.
30. Güner, O., O. Clement, and Y. Kurogi. 2004. Pharmacophore modeling and three dimensional database searching for drug design using catalyst: Recent advances. Curr. Med. Chem. 11(22): 2991–3005.
31. Güner, O. F. and J. P. Bowen. 2014. Setting the record straight: The origin of the pharmacophore concept. J. Chem. Inf. Model. 54(5): 1269–1283.
32. Hawkins, P. C. and A. Nicholls. 2012. Conformer generation with OMEGA: Learning from the data set and the analysis of failures. J. Chem. Inf. Model. 52(11): 2919–2936.
33. Hawkins, P. C. D., A. G. Skillman, G. L. Warren, B. A. Ellingson, and M. T. Stahl. 2010. Conformer generation with OMEGA: Algorithm and validation using high quality structures from the protein databank and Cambridge structural database. J. Chem. Inf. Model. 50(4): 572–584.
34. Heikamp, K. and J. Bajorath. 2013. Comparison of confirmed inactive and randomly selected compounds as negative training examples in support vector machinebased virtual screening. J. Chem. Inf. Model. 53(7): 1595–1601.
35. Hessler, G. and K. H. Baringhaus. 2010. The scaffold hopping potential of pharmacophores. Drug Discov. Today Technol. 7(4): e263–e269.
36. Horvath, D. 2011. Pharmacophore-based virtual screening. In: Chemoinformatics and Computational Chemical Biology, J. Bajorath (ed.), New York, NY: Humana Press, pp. 261–298.
37. Huang, N., B. K. Shoichet, and J. J. Irwin. 2006. Benchmarking sets for molecular docking. J. Med. Chem. 49(23): 6789–6801.
38. Hubbard, R. E. and H. M. Kamran. 2010. Hydrogen bonds in proteins: Role and strength. In: eLS, J. M. Valpuesta (ed.), Chichester: John Wiley & Sons, Ltd. DOI: 10.1002/9780470015902.a0003011.pub2.
39. Jacobsson, M., P. Liden, E. Stjernschantz, H. Bostrom, and U. Norinder. 2003. Improving structure-based virtual screening by multivariate analysis of scoring data. J. Med. Chem. 46(26): 5781–5789.
40. Jones, G., P. Willett and R. C. Glen. 2000. GASP: Genetic Algorithm Superimposition Program. In: Pharmacophore Perception, Development, and Use in Drug Design, O. F. Güner (ed.), La Jolla, CA: International University Line, pp. 85–106.
41. Kier, L. B. 1967. Molecular orbital calculation of preferred conformations of acetylcholine, muscarine, and muscarone. Mol. Pharmacol. 3(5): 487–494.
42. Kirchmair, J., G. Wolber, C. Laggner, and T. Langer. 2006. Comparative performance assessment of the conformational model generators omega and catalyst: A large-scale survey on the retrieval of protein-bound ligand conformations. J. Chem. Inf. Model. 46(4): 1848–1861.
43. Kirchmair, J., P. Markt, S. Distinto, G. Wolber, and T. Langer. 2008. Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection—What can we learn from earlier mistakes? J. Comput. Aided Mol. Des. 22(3–4): 213–228.
44. Koes, D. R. and C. J. Camacho. 2011. Pharmer: Efficient and exact pharmacophore search. J. Chem. Inf. Model. 51(6): 1307–1314.
45. Koes, D. R. and J. C. Carlos. 2012. ZINCPharmer: Pharmacophore search of the ZINC database. Nucleic Acids Res. 40(Web Server issue): W409–W414.
46. Kryger, G., I. Silman and J. L. Sussman. 1999. Structure of acetylcholinesterase complexed with E2020 (Aricept): Implications for the design of new anti-Alzheimer drugs. Structure 7(3): 297–307.
47. Kuhn, M., C. Von-Mering, M. Campillos, L. J. Jensen, and P. Bork. 2008. STITCH: Interaction networks of chemicals and proteins. Nucleic Acids Res. 36(Database issue): D684–D688.
48. Kuhn, M., D. Szklarczyk, A. Franceschini et al. 2010. STITCH 2: An interaction network database for small molecules and proteins. Nucleic Acids Res. 38(Database issue): D552–D556.
49. Kuhn, M., D. Szklarczyk, A. Franceschini, C. Von-Mering, L. J. Jensen, and P. Bork. 2012. STITCH 3: Zooming in on protein–chemical interactions. Nucleic Acids Res. 40(Database issue): D876–D880.
50. Kuhn, M., D. Szklarczyk, S. Pletscher-Frankild et al. 2014. STITCH 4: Integration of protein–chemical interactions with user data. Nucleic Acids Res. 42(Database issue): D401–D407.
51. Kurogi, Y. and O. F. Güner. 2004. Pharmacophore modeling and three-dimensional database searching for drug design using catalyst. Curr. Med. Chem. 8(9): 1035–1055.
52. Law, V., C. Knox, Y. Djoumbou et al. 2014. DrugBank 4.0: Shedding new light on drug metabolism. Nucleic Acids Res. 42(Database issue): D1091–D1097.
53. Leach, A. R., V. J. Gillet, R. A. Lewis, and R. Taylor. 2010. Three-dimensional pharmacophore methods in drug discovery. J. Med. Chem. 53(2): 539–558.
54. Li, H., J. Sutter, and R. Hoffman. 2000. HypoGen: An automated system for generating 3D predictive pharmacophore models. In: Pharmacophore Perception, Development and Use in Drug Design, O. F. Güner (ed.), La Jolla, CA: International University Line, pp. 171–190.
55. Li, S., Y. Xu, Q. Shen et al. 2013. Non-covalent interactions with aromatic rings: Current understanding and implications for rational drug design. Curr. Pharm. Des. 19(36): 6522–6533.
56. Ligand Scout 3.1 User Manual, Inte:Ligand GmbH, Vienna, Austria, 1999–2015, www.inteligand.com.
57. Lipinski, C. A. 2004. Lead- and drug-like compounds: The rule-of-five revolution. Drug Discov. Today Technol. 1(4): 337–341.
58. Lipinski, C. A., F. Lombardo, B. W. Dominy, and P. J. Feeney. 2001. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug. Deliv. Rev. 46(1–3): 3–26.
59. Liu, T., Y. Lin, X. Wen, R. N. Jorissen, and M. K. Gilson. 2007. BindingDB: A webaccessible database of experimentally determined protein–ligand binding affinities. Nucleic Acids Res. 35(Database issue): D198–D201.
60. Liu, X., S. Ouyang, B. Yu et al. 2010. PharmMapper server: A web server for potential drug target identification using pharmacophore mapping approach. Nucleic Acids Res. 38(Web Server issue): W609–W614.
61. Liu, Z., H. Fang, K. Reagan et al. 2013. In silico drug repositioning—What we need to know. Drug Discov. Today 18(3–4): 110–115.
62. Markt, P., R. K. Petersen, E. N. Flindt et al. 2008. Discovery of novel PPAR ligands by a virtual screening approach based on pharmacophore modeling, 3D shape, and electrostatic similarity screening. J. Med. Chem. 51(20): 6303–6317.
63. Nashev, L. G., D. Schuster, C. Laggner et al. 2010. The UV-filter benzophenone-1 inhibits 17β-hydroxysteroid dehydrogenase type 3: Virtual screening as a strategy to identify potential endocrine disrupting chemicals. Biochem. Pharmacol. 79(8): 1189–1199.
64. Olah, M., M. Mracec, L. Ostopovici et al. 2005. WOMBAT: World of molecular bioactivity. In: Chemoinformatics in Drug Discovery, B. Jürgen (ed.), Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, pp. 221–239.
65. Olah, M., R. Rad, L. Ostopovici et al. 2008. WOMBAT and WOMBAT-PK: Bioactivity databases for lead and drug discovery. In: Chemical Biology, L. S. Stuart, M. K. Tarun, and W. Günther (eds.), Weinheim, Germany: Wiley-VCH Verlag GmbH, pp. 760–786.
66. Olah, M. and T. I. Oprea. 2007. 3.14–Bioactivity databases. In: Comprehensive Medicinal Chemistry II, J. B. Taylor, and D. J. Triggle. (eds.), Oxford: Elsevier, pp. 293–313.
67. OMEGA 2.5.1.4: OpenEye Scientific Software, Santa Fe, NM. http://www.eyesopen.com. Hawkins, P.C.D.; Skillman, A.G.; Warren, G.L.; Ellingson, B.A.; Stahl, M.T.Ooms, F. 2000. Molecular modeling and computer aided drug design. Examples of their applications in medicinal chemistry. Curr. Med. Chem. 7(2): 141–158.
68. Oprea, T. I., A. M. Davis, S. J. Teague, and P. D. Leeson. 2001. Is there a difference between leads and drugs? A historical perspective. J. Chem. Inf. Comput. Sci. 41(5): 1308–1315.
69. Ortuso, F., T. Langer, and S. Alcaro. 2006. GBPM: GRID-based pharmacophore model: Concept and application studies to protein-protein recognition. Bioinformatics 22(12): 1449–1455.
70. Perez-Pineiro, R., A. Burgos, D. C. Jones et al. 2009. Development of a novel virtual screening cascade protocol to identify potential trypanothione reductase inhibitors. J. Med. Chem. 52(6): 1670–1680.
71. Polgar, T., A. Baki, G. I. Szendrei, and G. M. Keseru. 2005. Comparative virtual and experimental high-throughput screening for glycogen synthase kinase-3beta inhibitors. J. Med. Chem. 48(25): 7946–7959.
72. Reddy, A. S., S. P. Pati, P. P. Kumar, H. N. Pradeep, and G. N. Sastry. 2007. Virtual screening in drug discovery—A computational perspective. Curr. Protein Pept. Sci. 8(4): 329–351.
73. Richmond, N. J., C. A. Abrams, P. R. Wolohan, E. Abrahamian, P. Willett, and R. D. Clark. 2006. GALAHAD: 1. pharmacophore identification by hypermolecular alignment of ligands in 3D. J. Comput. Aided Mol. Des. 20(9): 567–587.
74. Rollinger, J. M., D. Schuster, B. Danzl et al. 2009. In silico target fishing for rationalized ligand discovery exemplified on constituents of Ruta graveolens. Planta Med. 75(3): 195–204.
75. Schneidman-Duhovny, D., O. Dror, Y. Inbar, R. Nussinov, and H. J. Wolfson. 2008. PharmaGist: A webserver for ligand-based pharmacophore detection. Nucleic Acids Res. 36(Web Server issue): W223–W228.
76. Schueler, F.W. 1961. Chemobiodynamics and drug design. J. Pharm. Sci. 50(1): 92–92.
77. Schuster, D. 2010. 3D pharmacophores as tools for activity profiling. Drug Discov. Today Technol. 7(4): e205–e211.
78. Schuster, D., E. M. Maurer, C. Laggner et al. 2006. The discovery of new 11β-Hydroxysteroid dehydrogenase type 1 inhibitors by common feature pharmacophore modeling and virtual screening. J. Med. Chem. 49(12): 3454–3466.
79. Schuster, D. and G. Wolber. 2010. Identification of bioactive natural products by pharmacophore- based virtual screening. Curr. Pharm. Des. 16(15): 1666–1681.
80. Schuster, D., M. Spetea, M. Music et al. 2010. Morphinans and isoquinolines: Acetylcholinesterase inhibition, pharmacophore modeling, and interaction with opioid receptors. Bioorg. Med. Chem. 18(14): 5071–5080.
81. Schwab, C. H. 2010. Conformations and 3D pharmacophore searching. Drug Discov. Today Technol. 7(4): e245–e253.
82. Spitzer, G. M., M. Heiss, M. Mangold et al. 2010. One concept, three implementations of 3D pharmacophore-based virtual screening: Distinct coverage of chemical search space. J. Chem. Inf. Model. 50(7): 1241–1247.
83. Sprague, P. W. 1995. Automated chemical hypothesis generation and database searching with Catalyst®. Perspect. Drug Discov. Des. 3(1): 1–20.
84. Steindl, T., C. Laggner, and T. Langer. 2005. Human rhinovirus 3C protease: Generation of pharmacophore models for peptidic and nonpeptidic inhibitors and their application in virtual screening. J. Chem. Inf. Model. 45(3): 716–724.
85. Steindl, T. M., D. Schuster, C. Laggner, and T. Langer. 2006. Parallel screening: A novel concept in pharmacophore modeling and virtual screening. J. Chem. Inf. Model. 46(5): 2146–2157.
86. Sutter, J., J. Li, A. J. Maynard, A. Goupil, T. Luu, and K. Nadassy. 2011. New features that improve the pharmacophore tools from Accelrys. Curr. Comput. Aided Drug Des. 7(3): 173–180.
87. Tanrikulu, Y., B. Krüger, and E. Proschak. 2013. The holistic integration of virtual screening in drug discovery. Drug Discov. Today 18(7–8): 358–364.
88. Temml, V., C. V. Voss, V. M. Dirsch, and D. Schuster. 2014. Discovery of new liver X receptor agonists by pharmacophore modeling and shape-based virtual screening. J. Chem. Inf. Model. 54(2): 367–371.
89. Toba, S., J. Srinivasan, A. J. Maynard, and J. Sutter. 2006. Using pharmacophore models to gain insight into structural binding and virtual screening: An application study with CDK2 and human DHFR. J. Chem. Inf. Model. 46(2): 728–735.
90. Todorov, N. P., I. L. Alberts, I. J. P. De-Esch, and P. M. Dean. 2007. QUASI: A novel method for simultaneous superposition of multiple flexible ligands and virtual screening using partial similarity. J. Chem. Inf. Model. 47(3): 1007–1020.
91. Triballeau, N., F. Acher, I. Brabet, J. P. Pin, and H. O. Bertrand. 2005. Virtual screening workflow development guided by the “eceiver operating characteristic” curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4. J. Med. Chem. 48(7): 2534–2547.
92. Tripos International, SYBYL 8.0, 1699 South Hanley Rd., St. Louis, Missouri, 63144, USA.
93. Verdonk, M. L., V. Berdini, M. J. Hartshorn et al. 2004. Virtual screening using protein− ligand docking: Avoiding artificial enrichment. J. Chem. Inf. Comput. Sci. 44(3): 793–806.
94. Vuorinen, A., L. G. Nashev, A. Odermatt, J. M. Rollinger, and D. Schuster. 2014. Pharmacophore model refinement for 11β-Hydroxysteroid dehydrogenase inhibitors: Search for modulators of intracellular glucocorticoid concentrations. Mol. Inf. 33(1): 15–25.
95. Wang, Q., J. Park, A. K. Devkota, E. J. Cho, K. N. Dalby, and P. Ren. 2014. Identification and validation of novel PERK inhibitors. J. Chem. Inf. Model. 54(5): 1467–1475.
96. Wang, Y., E. Bolton, S. Dracheva et al. 2010. An overview of the PubChem BioAssay resource. Nucleic Acids Res. 38(Database issue): D255–D266.
97. Wermuth, C. G., C. R. Ganellin, P. Lindberg, and L. A. Mitscher. 1998. Glossary of terms used in medicinal chemistry (IUPAC reccomendations 1998). Pure Appl. Chem. 70(5): 1129–1143.
98. Wilcken, R., M. O. Zimmermann, A. Lange, A. C. Joerger, and F. M. Boeckler. 2013. Principles and applications of halogen bonding in medicinal chemistry and chemical biology. J. Med. Chem. 56(4): 1363–1388.
99. Williams, A. J., L. Harland, P. Groth et al. 2012. Open PHACTS: Semantic interoperability for drug discovery. Drug Discov. Today 17(21–22): 1188–1198.
100. Wolber, G., A. Dornhofer, and T. Langer. 2006. Efficient overlay of small organic molecules using 3D pharmacophores. J. Comput. Aided Mol. Des. 20(12): 773–788.
101. Wolber, G. and T. Langer. 2005. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J. Chem. Inf. Model. 45(1): 160–169.
102. Young, S. M., C. Bologa, E. R. Prossnitz, T. I. Oprea, L. A. Sklar, and B. S. Edwards. 2005. High-throughput screening with HyperCyt flow cytometry to detect small molecule formylpeptide receptor ligands. J. Biomol. Screen. 10(4): 374–382.
103. Zweig, M. H. and G. Campbell. 1993. Receiver-operating characteristic (ROC) plots: A fundamental evaluation tool in clinical medicine. Clin. Chem. 39(4): 561–577.