Medicinal Chemistry and Ligand Profiling for Evaluation of Promising Marine Bioactive Molecules

  • A. K. Croft
  • W. Groenewald
  • M. S. Tierney


Many marine natural products exhibit a range of bioactivities, including anticancer, antiviral, antifungal, and antihypertensive properties. As such, they are excellent lead compounds for further drug discovery. In recent years, due to the more accessible cost of computing in terms of both money and time, the complex and expensive process of drug discovery has been significantly enhanced through the use of computational approaches. Here we describe key aspects of the process where computation has helped, including lead validation, optimization, profiling, and discovery, as well as in silico ADME (absorption, distribution, metabolism, and elimination) and toxicological methods, giving relevant examples of the uses of such approaches from the marine natural product world.


Partial Little Square Molecular Docking Virtual Screening Pharmacophore Model Drug Discovery Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Abraham, I., S. Jain, C.P. Wu, M.A. Khanfar, Y. Kuang, C.L. Dai, Z. Shi, X. Chen, L. Fu, S.V. Ambudkar, K. El Sayed, and Z.S. Chen. 2010. Marine sponge-derived sipholane triterpenoids reverse P-glycoprotein (ABCB1)-mediated multidrug resistance in cancer cells. Biochemical Pharmacology 80: 1497–1506.PubMedCrossRefGoogle Scholar
  2. Andersen, R.J., D.J. Faulkner, C.H. He, G.D. Van Duyne, and J. Clardy. 1985. Metabolites of the marine prosobranch mollusk Lamellaria sp. Journal of the American Chemical Society 107: 5492–5495.CrossRefGoogle Scholar
  3. Babu, P.A., S.S. Puppala, S.L. Aswini, M.R. Vani, C.N. Kumar, and T. Prasanna. 2008. A database of natural products and chemical entities from marine habitat. Bioinformation 3: 142–143.PubMedGoogle Scholar
  4. Baunbaek, D., N. Trinkler, Y. Ferandin, O. Lozach, P. Ploypradith, S. Rucirawat, F. Ishibashi, M. Iwao, and L. Meijer. 2008. Anticancer alkaloid lamellarins inhibit protein kinases. Marine Drugs 6: 514–527.PubMedCrossRefGoogle Scholar
  5. Beierlein, F., H. Lanig, G. Schurer, A.H.C. Horn, and T. Clark. 2003. Quantum mechanical/molecular mechanical (QM/MM) docking: An evaluation for known test systems. Molecular Physics 101: 2469–2480.CrossRefGoogle Scholar
  6. Belden, H. 2005. First pain drug in new class comes from snail. Drug Topics 149: 8.Google Scholar
  7. Bissantz, C., G. Folkers, and D. Rognan. 2000. Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations. Journal of Medicinal Chemistry 43: 4759–4767.PubMedCrossRefGoogle Scholar
  8. Blundell, T.L., H. Jhoti, and C. Abell. 2002. High-throughput crystallography for lead discovery in drug design. Nature Reviews Drug Discovery 1: 45–54.PubMedCrossRefGoogle Scholar
  9. Boldi, A.M. 2004. Libraries from natural product-like scaffolds. Current Opinion in Chemical Biology 8: 281–286.PubMedCrossRefGoogle Scholar
  10. Bradley, J.C., C. Neylon, R. Guha, A.J. Williams, B. Hooker, A.S.I.D. Lang, B. Friesen, T. Bohinski, D. Bulger, M. Federici, J. Hale, J. Mancinelli, K.B. Mirza, M.J. Moritz, D. Rein, C. Tchakounte, and H.T. Truong. 2010. Open notebook science challenge: Solubilities of organic compounds in organic solvents. Nature Precedings. doi: 10.1038/npre.2010.4243.1033.
  11. Bugni, T.S., B. Richards, L. Bhoite, D. Cimbora, M.K. Harper, and C.M. Ireland. 2008. Marine natural product libraries for high-throughput screening and rapid drug discovery. Journal of Natural Products 71: 1095–1098.PubMedCrossRefGoogle Scholar
  12. Butina, D., M.D. Segall, and K. Frankcombe. 2002. Predicting ADME properties in silico: Methods and models. Drug Discovery Today 7: S83–S88.PubMedCrossRefGoogle Scholar
  13. Cavalli, A., M.L. Bolognesi, A. Minarini, M. Rosini, V. Tumiatti, M. Recanatini, and C. Melchiorre. 2008. Multi-target-directed ligands to combat neurodegenerative diseases. Journal of Medicinal Chemistry 51: 347–372.PubMedCrossRefGoogle Scholar
  14. Chong, C.R., and D.J. Sullivan. 2007. New uses for old drugs. Nature 448: 645–646.PubMedCrossRefGoogle Scholar
  15. Clardy, J., and C. Walsh. 2004. Lessons from natural molecules. Nature 432: 829–837.PubMedCrossRefGoogle Scholar
  16. Claudel, T., B. Staels, and F. Kuipers. 2005. The Farnesoid X receptor: A molecular link between bile acid and lipid and glucose metabolism. Arteriosclerosis, Thrombosis, and Vascular Biology 25: 2020–2030.PubMedCrossRefGoogle Scholar
  17. Cole, S.L., and R. Vassar. 2007. The Alzheimer’s disease β-secretase enzyme, BACE1. Molecular Neurodegeneration 2: 22.PubMedCrossRefGoogle Scholar
  18. Congreve, M., R. Carr, C. Murray, and H. Jhoti. 2003. A rule of three for fragment-based lead discovery? Drug Discovery Today 8: 876–877.PubMedCrossRefGoogle Scholar
  19. Congreve, M., G. Chessari, D. Tisi, and A.J. Woodhead. 2008. Recent developments in fragment-based drug discovery. Journal of Medicinal Chemistry 51: 3661–3680.PubMedCrossRefGoogle Scholar
  20. Cragg, G.M., S.A. Schepartz, M. Suffness, and M.R. Grever. 1993. The taxol supply crisis. New NCI policies for handling the large-scale production of novel natural product anticancer and anti-HIV agents. Journal of Natural Products 56: 1657–1668.PubMedCrossRefGoogle Scholar
  21. Davis, A.M., and R.J. Riley. 2004. Predictice ADMET studies, the challenges and the opportunities. Current Opinion in Chemical Biology 8: 378–386.PubMedCrossRefGoogle Scholar
  22. Davis, G.D., and A.H. Vasanthi. 2011. Seaweed metabolite database (SWMD): A database of natural compounds from marine algae. Bioinformation 5: 361–364.PubMedGoogle Scholar
  23. de Groot, M.J. 2006. Designing better drugs: Predicting cytochrome P450 metabolism. Drug Discovery Today 11: 601–606.PubMedCrossRefGoogle Scholar
  24. Deng, X.Q., H.Y. Wang, Y.L. Zhao, M.L. Xiang, P.D. Jiang, Z.X. Cao, Y.Z. Zheng, S.D. Luo, L.T. Yu, Y.Q. Wei, and S.Y. Yang. 2008. Pharmacophore modelling and virtual screening for identification of new Aurora-A kinase inhibitors. Chemical Biology & Drug Design 71: 533–539.CrossRefGoogle Scholar
  25. Dictionary of Natural Products. London: Chapman & Hall/CRC Informa.Google Scholar
  26. Do, Q.T., C. Lamy, I. Renimel, N. Sauvan, P. Andre, F. Himbert, L. Morin-Allory, and P. Bernard. 2007. Reverse pharmacognosy: Identifying biological properties for plants by means of their molecule constituents: Application to meranzin. Planta Medica 73: 1235–1240.PubMedCrossRefGoogle Scholar
  27. 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. Journal of Chemical Information and Modeling 49: 2333–2343.PubMedCrossRefGoogle Scholar
  28. Eisenhauer, E.A., W.W. ten Bokkel Huinink, K.D. Swenerton, L. Gianni, J. Myles, M.E. van der Burg, I. Kerr, J.B. Vermorken, K. Buser, and N. Colombo. 1994. European-Canadian randomized trial of paclitaxel in relapsed ovarian cancer: High-dose versus low-dose and long versus short infusion. Journal of Clinical Oncology 12: 2654–2666.PubMedGoogle Scholar
  29. Ekins, S., J.D. Honeycutt, and J.T. Metz. 2010. Evolving molecules using multi-objective optimization: Applying to ADME/Tox. Drug Discovery Today 15: 451–460.PubMedCrossRefGoogle Scholar
  30. Eldridge, M.D., C.W. Murray, T.R. Auton, G.V. Paolini, and R.P. Mee. 1997. Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. Journal of Computer-Aided Molecular Design 11: 425–445.PubMedCrossRefGoogle Scholar
  31. Erlanson, D.A., R.S. McDowell, and T. O’Brien. 2004. Fragment-based drug discovery. Journal of Medicinal Chemistry 47: 3463–3482.PubMedCrossRefGoogle Scholar
  32. Ertl, P., S. Roggo, and A. Schuffenhauer. 2008. Natural product-likeness score and its application for prioritization of compound libraries. Journal of Chemical Information and Modeling 48: 68–74.PubMedCrossRefGoogle Scholar
  33. Ewing, T.J., S. Makino, A.G. Skillman, and I.D. Kuntz. 2001. DOCK 4.0: Search strategies for automated molecular docking of flexible molecule databases. Journal of Computer-Aided Molecular Design 15: 411–428.PubMedCrossRefGoogle Scholar
  34. Fanfrlik, J., J. Brynda, J. Rezac, P. Hobza, and M. Lepsik. 2008. Interpretation of protein/ligand crystal structure using QM/MM calculations: Case of HIV-1 protease/metallacarborane complex. Journal of Physical Chemistry B 112: 15094–15102.CrossRefGoogle Scholar
  35. Feher, M., and J.M. Schmidt. 2003. Property distributions: Differences between drugs, natural products, and molecules from combinatorial chemistry. Journal of Chemical Information and Computer Sciences 43: 218–227.PubMedCrossRefGoogle Scholar
  36. Ferrara, P., H. Gohlke, D.J. Price, G. Klebe, and C.L. Brooks III. 2004. Assessing scoring functions for protein. Journal of Medicinal Chemistry 47: 3032–3047.PubMedCrossRefGoogle Scholar
  37. Foster, I., Zhao, Y., Raicu, I., and Lu, S. 2009. Cloud computing and grid computing 360-degree compared. ArXiv e-prints: 0901.0131v0901.
  38. Francis, G.A., E. Fayard, F. Picard, and J. Auwerx. 2003. Nuclear receptors and the control of metabolism. Annual Review of Physiology 65: 261–311.PubMedCrossRefGoogle Scholar
  39. FRED. 2008. OpenEye Scientific Software. New Mexico, United States.Google Scholar
  40. Gentilucci, L., F. Squassabia, R. Demarco, R. Artali, G. Cardillo, A. Tolomelli, S. Spampinato, and A. Bedini. 2008. Investigation of the interaction between the atypical agonist c[YpwFG] and MOR. Febs Journal 275: 2315–2337.PubMedCrossRefGoogle Scholar
  41. Glaser, K.B., and A.M. Mayer. 2009. A renaissance in marine pharmacology: From preclinical curiosity to clinical reality. Biochemical Pharmacology 78: 440–448.PubMedCrossRefGoogle Scholar
  42. Gleeson, M.P. 2008. Generation of a set of simple, interpretable ADMET rules of thumb. Journal of Medicinal Chemistry 51: 817–834.PubMedCrossRefGoogle Scholar
  43. Gleeson, M.P., and D. Gleeson. 2009. QM/MM calculations in drug discovery: A useful method for studying binding phenomena? Journal of Chemical Information and Modeling 49: 670–677.PubMedCrossRefGoogle Scholar
  44. Gleeson, M.P., S. Hannongbua, and D. Gleeson. 2010. QM methods in structure based design: Utility in probing protein-ligand interactions. Journal of Molecular Graphics and Modelling 29: 507–517.PubMedCrossRefGoogle Scholar
  45. Guner, O.F. 2002. History and evolution of the pharmacophore concept in computer-aided drug design. Current Topics in Medicinal Chemistry 2: 1321–1332.PubMedCrossRefGoogle Scholar
  46. Hai-Lun, H., C. Xiu-Lan, S. Cai-Yun, Z. Yu-Zhong, and Z. Bai-Cheng. 2006. Analysis of novel angiotensin-I-converting enzyme inhibitory peptides from protease-hydrolyzed marine shrimp Acetes chinensis. Journal of Peptide Science 12: 726–733.PubMedCrossRefGoogle Scholar
  47. Halgren, T.A., and W. Damm. 2001. Polarizable force fields. Current Opinion in Structural Biology 11: 236–242.PubMedCrossRefGoogle Scholar
  48. Hardy, B., OpenTox. 2011. Barry Hardy. Accessed 3 May 2011.
  49. Harriman, D.J., and G. Deslongehamps. 2004. Reverse-docking as a computational tool for the study of asymmetric organocatalysis. Journal of Computer-Aided Molecular Design 18: 303–308.PubMedCrossRefGoogle Scholar
  50. Harvey, A.L. 2007. Natural products as a screening resource. Current Opinion in Chemical Biology 11: 480–484.PubMedCrossRefGoogle Scholar
  51. Haustedt, L.O., C. Mang, K. Siems, and H. Schiewe. 2006. Rational approaches to natural-product-based drug design. Current Opinion in Drug Discovery & Development 9: 445–462.Google Scholar
  52. Hein, M., D. Zilian, and C.A. Sotriffer. 2010. Docking compared to 3D-pharmacophores: The scoring function challenge. Drug Discovery Today: Technologies 7: e229–e236.CrossRefGoogle Scholar
  53. Hopfinger, A.J., A. Reaka, P. Venkatarangan, J.S. Duca, and S. Wang. 1999. Construction of a virtual high throughput screen by 4D-QSAR analysis: Application to a combinatorial library of glucose inhibitors of glycogen phosphorylase b. Journal of Chemical Information and Computer Sciences 39: 1151–1160.CrossRefGoogle Scholar
  54. Huang, N., B.K. Shoichet, and J.J. Irwin. 2006. Benchmarking sets for molecular docking. Journal of Medicinal Chemistry 49: 6789–6801.PubMedCrossRefGoogle Scholar
  55. Ibrahim, M.A., A.G. Shilabin, S. Prasanna, M. Jacob, S.I. Khan, R.J. Doerksen, and M.T. Hamann. 2008. 2-N-methyl modifications and SAR studies of manzamine A. Bioorganic and Medicinal Chemistry 16: 6702–6706.PubMedCrossRefGoogle Scholar
  56. Indarte, M., J.D. Madura, and C.K. Surratt. 2007. Dopamine transporter comparative molecular modeling and binding site prediction using the LeuTAa leucine transporter as a template. Proteins: Structure, Function, and Bioinformatics 70: 1033–1046.CrossRefGoogle Scholar
  57. Jain, A.N. 2003. Surflex: Fully automatic flexible molecular docking using a molecular similarity-based search engine. Journal of Medicinal Chemistry 46: 499–511.PubMedCrossRefGoogle Scholar
  58. Jain, S., I. Abraham, P. Carvalho, Y.H. Kuang, L.A. Shaala, D.T. Youssef, M.A. Avery, Z.S. Chen, and K.A. El Sayed. 2009. Sipholane triterpenoids: Chemistry, reversal of ABCB1/P-glycoprotein-mediated multidrug resistance, and pharmacophore modeling. Journal of Natural Products 72: 1291–1298.PubMedCrossRefGoogle Scholar
  59. Jang, J.-H., S.-C. Jeong, J.-H. Kim, Y.-H. Lee, Y.-C. Ju, and J.-S. Lee. 2011. Characterisation of a new antihypertensive angiotensin I-converting enzyme inhibitory peptide from Pleurotus cornucopiae. Food Chemistry 127: 412–418.CrossRefGoogle Scholar
  60. Jansen, J.M., and E.J. Martin. 2004. Target-biased scoring approaches and expert systems in structure-based virtual screening. Current Opinion in Chemical Biology 8: 359–364.PubMedCrossRefGoogle Scholar
  61. Jimsheena, V.K., and L.R. Gowda. 2010. Arachin derived peptides as selective angiotensin I-converting enzyme (ACE) inhibitors: Structure-activity relationship. Peptides 31: 1165–1176.PubMedCrossRefGoogle Scholar
  62. Jimsheena, V.K., and L.R. Gowda. 2011. Angiotensin I-converting enzyme (ACE) inhibitory peptides derived from arachin by simulated gastric digestion. Food Chemistry 125: 561–569.CrossRefGoogle Scholar
  63. John, S., S. Thangapandian, S. Sakkiah, and K.W. Lee. 2011. Potent bace-1 inhibitor design using pharmacophore modeling, in silico screening and molecular docking studies. BMC Bioinformatics 12: S1–S28.CrossRefGoogle Scholar
  64. Jones, G., P. Willett, R.C. Glen, A.R. Leach, and R. Taylor. 1997. Development and validation of a genetic algorithm for flexible docking. Journal of Molecular Biology 267: 727–748.PubMedCrossRefGoogle Scholar
  65. Jung, H.A., S.K. Hyun, H.R. Kim, and J.S. Choi. 2006. Angiotensin-converting enzyme I inhibitory activity of phlorotannins from Ecklonia stolonifera. Fisheries Science 72: 1292–1299.CrossRefGoogle Scholar
  66. Jung, H.A., S.H. Oh, and J.S. Choi. 2010. Molecular docking studies of phlorotannins from Eisenia bicyclis with BACE1 inhibitory activity. Bioorganic and Medicinal Chemistry Letters 20: 3211–3215.PubMedCrossRefGoogle Scholar
  67. Kahnberg, P., M.H. Howard, T. Liljefors, M. Nielsen, E.O. Nielsen, O. Sterner, and I. Pettersson. 2004. The use of a pharmacophore model for identification of novel ligands for the benzodiazepine binding site of the GABAA receptor. Journal of Molecular Graphics and Modelling 23: 253–261.PubMedCrossRefGoogle Scholar
  68. Kassel, D.B. 2004. Applications of high-throughput ADME in drug discovery. Current Opinion in Chemical Biology 8: 339–345.PubMedCrossRefGoogle Scholar
  69. Keseru, G.M., and G.M. Makara. 2006. Hit discovery and hit-to-lead approaches. Drug Discovery Today 11: 741–748.PubMedCrossRefGoogle Scholar
  70. Khanfar, M.A., B.A. Asal, M. Mudit, A. Kaddoumi, and K.A. El Sayed. 2009. The marine natural-derived inhibitors of glycogen synthase kinase-3β phenylmethylene hydantoins: In vitro and in vivo activities and pharmacophore modeling. Bioorganic and Medicinal Chemistry 17: 6032–6039.PubMedCrossRefGoogle Scholar
  71. Khanfar, M.A., R.A. Hill, A. Kaddoumi, and K.A. El Sayed. 2010. Discovery of novel GSK-3beta inhibitors with potent in vitro and in vivo activities and excellent brain permeability using combined ligand- and structure-based virtual screening. Journal of Medicinal Chemistry 53: 8534–8545.PubMedCrossRefGoogle Scholar
  72. Kim, A., T. Shin, M. Lee, J. Park, K. Park, N. Yoon, J. Kim, J. Choi, B. Jang, D. Byun, N. Park, and H. Kim. 2009. Isolation and identification of phlorotannins from Ecklonia stolonifera with antioxidant and anti-inflammatory properties. Journal of Agricultural and Food Chemistry 57: 3483–3489.PubMedCrossRefGoogle Scholar
  73. 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? Journal of Computer-Aided Molecular Design 22: 213–228.PubMedCrossRefGoogle Scholar
  74. Klabunde, T., and A. Evers. 2005. GPCR antitarget modeling: Pharmacophore models for biogenic amine binding GPCRs to avoid GPCR-mediated side effects. ChemBioChem: A European Journal of Chemical Biology 6: 876–889.CrossRefGoogle Scholar
  75. Koch, M.A., L.O. Wittenberg, S. Basu, D.A. Jeyaraj, E. Gourzoulidou, K. Reinecke, A. Odermatt, and H. Waldmann. 2004. Compound library development guided by protein structure similarity clustering and natural product structure. Proceedings of the National Academy of Sciences of the United States of America 101: 16721–16726.PubMedCrossRefGoogle Scholar
  76. Kortagere, S., and S. Ekins. 2010. Troubleshooting computational methods in drug discovery. Journal of Pharmacological and Toxicological Methods 61: 67–75.PubMedCrossRefGoogle Scholar
  77. Krishnaiah, P., V.L. Reddy, G. Venkataramana, K. Ravinder, M. Srinivasulu, T.V. Raju, K. Ravikumar, D. Chandrasekar, S. Ramakrishna, and Y. Venkateswarlu. 2004. New lamellarin alkaloids from the Indian ascidian Didemnum obscurum and their antioxidant properties. Journal of Natural Products 67: 1168–1171.PubMedCrossRefGoogle Scholar
  78. Kroemer, R.T., A. Vulpetti, J.J. McDonald, D.C. Rohrer, J.Y. Trosset, F. Giordanetto, S. Cotesta, C. McMartin, M. Kihlen, and P.F. Stouten. 2004. Assessment of docking poses: Interactions-based accuracy classification (IBAC) versus crystal structure deviations. Journal of Chemical Information and Computer Sciences 44: 871–881.PubMedCrossRefGoogle Scholar
  79. Lei, J., and J. Zhou. 2002. A marine natural product database. Journal of Chemical Information and Computer Sciences 42: 742–748.PubMedCrossRefGoogle Scholar
  80. Levesque, M.J., K. Ichikawa, S. Date, and J.H. Haga. 2009. Design of a grid service-based platform for in silico protein-ligand screenings. Computer Methods and Programs in Biomedicine 93: 73–82.PubMedCrossRefGoogle Scholar
  81. Li, G.-H., G.-W. Le, Y.-H. Shi, and S. Shrestha. 2004. Angiotensin I–converting enzyme inhibitory peptides derived from food proteins and their physiological and pharmacological effects. Nutrition Research 24: 469–486.Google Scholar
  82. Li, X., F. Lu, Q. Tian, Y. Yang, Q. Wang, and J.Z. Wang. 2006. Activation of glycogen synthase kinase-3 induces Alzheimer-like tau hyperphosphorylation in rat hippocampus slices in culture. Journal of Neural Transmission 113: 93–102.PubMedCrossRefGoogle Scholar
  83. LigandScout, Inte: Ligand GmbH, Vienna, Austria, Europe.Google Scholar
  84. 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. Advanced Drug Delivery Reviews 46: 3–26.PubMedCrossRefGoogle Scholar
  85. Liu, X., S. Ouyang, B. Yu, Y. Liu, K. Huang, J. Gong, S. Zheng, Z. Li, H. Li, and H. Jiang. 2010. PharmMapper server: D web server for potential drug target identification using pharmacophore mapping approach. Nucleic Acids Research 38: W609–614.PubMedCrossRefGoogle Scholar
  86. Lizcano, F., C. Romero, and D. Vargas. 2011. Regulation of adipogenesis by nuclear receptor PPAR gamma is modulated by the histone demethylase JMJD2C. Genetics and Molecular Biology 34: 19–24.PubMedCrossRefGoogle Scholar
  87. Lyne, P.D., P.W. Kenny, D.A. Cosgrove, C. Deng, S. Zabludoff, J.J. Wendoloski, and S. Ashwell. 2004. Identification of compounds with nanomolar binding affinity for checkpoint kinase-1 using knowledge-based virtual screening. Journal of Medicinal Chemistry 47: 1962–1968.PubMedCrossRefGoogle Scholar
  88. Mang, C., S. Jakupovic, S. Schunk, H.D. Ambrosi, O. Schwarz, and J. Jakupovic. 2006. Natural products in combinatorial chemistry: An andrographolide-based library. Journal of Combinatorial Chemistry 8: 268–274.PubMedCrossRefGoogle Scholar
  89. MarinLit. Marine Natural Product Bibliography Software. Christchurch, New Zealand: University of Cantebury.Google Scholar
  90. Martin, Y.C., M.G. Bures, E.A. Danaher, J. DeLazzer, I. Lico, and P.A. Pavlik. 1993. A fast new approach to pharmacophore mapping and its application to dopaminergic and benzodiazepine agonists. Journal of Computer-Aided Molecular Design 7: 83–102.PubMedCrossRefGoogle Scholar
  91. Martinez, A., A. Castro, I. Dorronsoro, and M. Alonso. 2002. Glycogen synthase kinase 3 (GSK-3) inhibitors as new promising drugs for diabetes, neurodegeneration, cancer, and inflammation. Medicinal Research Reviews 22: 373–384.PubMedCrossRefGoogle Scholar
  92. Martins, J.P., E.G. Barbosa, K.F. Pasqualoto, and M.M. Ferreira. 2009. LQTA-QSAR: A new 4D-QSAR methodology. Journal of Chemical Information and Modeling 49: 1428–1436.PubMedCrossRefGoogle Scholar
  93. McGann, M. 2011. FRED pose prediction and virtual screening accuracy. Journal of Chemical Information and Modeling 51: 578–596.PubMedCrossRefGoogle Scholar
  94. McIntosh, M., L.J. Cruz, M.W. Hunkapiller, W.R. Gray, and B.M. Olivera. 1982. Isolation and structure of a peptide toxin from the marine snail Conus magus. Archives of Biochemistry and Biophysics 218: 329–334.PubMedCrossRefGoogle Scholar
  95. Mishra, K.P., L. Ganju, M. Sairam, P.K. Banerjee, and R.C. Sawhney. 2008. A review of high throughput technology for the screening of natural products. Biomedicine & Pharmacotherapy 62: 94–98.CrossRefGoogle Scholar
  96. Mladenovic, M., M. Arnone, R.F. Fink, and B. Engels. 2009. Environmental effects on charge densities of biologically active molecules: Do molecule crystal environments indeed approximate protein surroundings? Journal of Physical Chemistry B 13: 5072–5082.CrossRefGoogle Scholar
  97. Molecular Operating Environment, Chemical Computing Group (CCG), Montreal, Canada.Google Scholar
  98. Morphy, R., and Z. Rankovic. 2005. Designed multiple ligands. An emerging drug discovery paradigm. Journal of Medicinal Chemistry 48: 6523–6543.PubMedCrossRefGoogle Scholar
  99. Morris, G.M., D.S. Goodsell, R.S. Halliday, R. Huey, W.E. Hart, R.K. Belew, and A.J. Olson. 1998. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. Journal of Computational Chemistry 19: 1639–1662.CrossRefGoogle Scholar
  100. MTree, BioSolveIT Gmbh, Sankt Augustin, Germany.Google Scholar
  101. Muegge, I. 2000. A knowledge-based scoring function for protein-ligand interactions: Probing the reference state. Perspectives in Drug Discovery and Design 20: 99–114.CrossRefGoogle Scholar
  102. Mukherjee, P., P. Desai, A. Srivastava, B. Tekwani, and M. Avery. 2008. Probing the structures of leishmanial farnesyl pyrophosphate synthases: Homology modeling and docking studies. Journal of Chemical Information and Modeling 48: 1026–1040.PubMedCrossRefGoogle Scholar
  103. Murray, C.W., and T.L. Blundell. 2010. Structural biology in fragment-based drug design. Current Opinion in Structural Biology 20: 497–507.PubMedCrossRefGoogle Scholar
  104. Norinder, U., and C.A.S. Bergstrom. 2006. Prediction of ADMET properties. ChemMedChem 1: 920–937.PubMedCrossRefGoogle Scholar
  105. Nwosu, F., J. Morris, V.A. Lund, D. Stewart, H.A. Ross, and G.J. McDougall. 2011. Anti-proliferative and potential anti-diabetic effects of phenolic-rich extracts from edible marine algae. Food Chemistry 126: 1006–1012.CrossRefGoogle Scholar
  106. Ono, S., M. Hosokawa, K. Miyashita, and K. Takahashi. 2006. Isolation of peptides with angiotensin I-converting enzyme inhibitory effect derived from hydrolysate of upstream Chum Salmon Muscle. Journal of Food Science 68: 1611–1614.CrossRefGoogle Scholar
  107. Oprea, T.I., and H. Matter. 2004. Integrating virtual screening in lead discovery. Current Opinion in Chemical Biology 8: 349–358.PubMedCrossRefGoogle Scholar
  108. Parys, S., S. Kehraus, A. Krick, K.W. Glombitza, S. Carmeli, K. Klimo, C. Gerhauser, and G.M. Konig. 2010. In vitro chemopreventive potential of fucophlorethols from the brown alga Fucus vesiculosus L. by anti-oxidant activity and inhibition of selected cytochrome P450 enzymes. Phytochemistry 71: 221–229.PubMedCrossRefGoogle Scholar
  109. Patel, S., and C.L. Brooks III. 2006. Fluctuating charge force fields: Recent developments and applications from small molecules to macromolecular biological systems. Molecular Simulation 32: 231–249.CrossRefGoogle Scholar
  110. Peach, M.L., and M.C. Nicklaus. 2009. Combining docking with pharmacophore filtering for improved virtual screening. Journal of Cheminformatics 1: 6.PubMedCrossRefGoogle Scholar
  111. Pelish, H.E., N.J. Westwood, Y. Feng, T. Kirchhausen, and M.D. Shair. 2001. Use of biomimetic diversity-oriented synthesis to discover galanthamine-like molecules with biological properties beyond those of the natural product. Journal of the American Chemical Society 123: 6740–6741.PubMedCrossRefGoogle Scholar
  112. QSAR World. 2007. Strand Life Sciences Pvt. Ltd., Accessed 3 May 2011.
  113. Queiroz, A.N., B.A.Q. Gomes, W.M. Moraes Jr., and R.S. Borges. 2009. A theoretical antioxidant pharmacophore for resveratrol. European Journal of Medicinal Chemistry 44: 1644–1649.PubMedCrossRefGoogle Scholar
  114. Raha, K., M.B. Peters, B. Wang, N. Yu, A.M. Wollacott, L.M. Westerhoff, and K.M. Merz Jr. 2007. The role of quantum mechanics in structure-based drug design. Drug Discovery Today 12: 725–731.PubMedCrossRefGoogle Scholar
  115. Reddy, M.V., M.R. Rao, D. Rhodes, M.S. Hansen, K. Rubins, F.D. Bushman, Y. Venkateswarlu, and D.J. Faulkner. 1999. Lamellarin alpha 20-sulfate, an inhibitor of HIV-1 integrase active against HIV-1 virus in cell culture. Journal of Medicinal Chemistry 42: 1901–1907.PubMedCrossRefGoogle Scholar
  116. Rella, M., C.A. Rushworth, J.L. Guy, A.J. Turner, T. Langer, and R.M. Jackson. 2006. Structure-based pharmacophore design and virtual screening for novel angiotensin converting enzyme 2 inhibitors. Journal of Chemical Information and Modeling 46: 708–716.PubMedCrossRefGoogle Scholar
  117. Reymond, J.L., R. van Deursen, L.C. Blum, and L. Ruddigkeit. 2010. Chemical space as a source for new drugs. Medicinal Chemistry Communications 1: 30–38.CrossRefGoogle Scholar
  118. Rinehart, K.L., T.G. Holt, N.L. Fregeau, J.G. Stroh, P.A. Keifer, F. Sun, L.H. Li, and D.G. Martin. 1990. Ecteinascidins 729, 743, 745, 759A, 759B, and 770: Potent antitumor agents from the Caribbean tunicate Ecteinascidia turbinata. Journal of Organic Chemistry 55: 4512–4515.CrossRefGoogle Scholar
  119. Ripphausen, P., B. Nisius, L. Peltason, and J. Bajorath. 2010. Quo vadis, virtual screening? A comprehensive survey of prospective applications. Journal of Medicinal Chemistry 53: 8461–8467.PubMedCrossRefGoogle Scholar
  120. Ritchie, T.J., P. Ertl, and R. Lewis. 2011. The graphical representation of ADME-related molecule properties for medicinal chemists. Drug Discovery Today 16: 65–72.PubMedCrossRefGoogle Scholar
  121. Rollinger, J.M. 2009. Accessing target information by virtual parallel screening – The impact on natural product research. Phytochemistry Letters 2: 53–58.CrossRefGoogle Scholar
  122. Rollinger, J.M., A. Hornick, T. Langer, H. Stuppner, and H. Prast. 2004. Acetylcholinesterase inhibitory activity of scopolin and scopoletin discovered by virtual screening of natural products. Journal of Medicinal Chemistry 47: 6248–6254.PubMedCrossRefGoogle Scholar
  123. Sakai, R., T. Higa, C.W. Jefford, and G. Bernardinelli. 1986. Manzamine A, a novel antitumor alkaloid from a sponge. Journal of the American Chemical Society 108: 6404–6405.CrossRefGoogle Scholar
  124. Samantray, D., and R.K. Sahu. 2010. Drug designing and docking efficacy assessment of halogen substituted aspirin. Researcher 2: 17–23.Google Scholar
  125. Sato, M., T. Hosokawa, T. Yamaguchi, N. Toshiki, K. Muramoto, T. Kahara, K. Funayama, A. Kobayashi, and T. Nakano. 2002. Angiotensin I-converting enzyme inhibitory peptides derived from Wakame (Undaria pinnatifida) and their antihypertensive effect in spontaneously hypertensive Rats. Journal of Agricultural and Food Chemistry 50: 6245–6252.PubMedCrossRefGoogle Scholar
  126. Schneidman-Duhovny, D., O. Dror, Y. Inbar, R. Nussinov, and H.J. Wolfson. 2008. PharmaGist: A webserver for ligand-based pharmacophore detection. Nucleic Acids Research 36: W223–W228. Catalyst, Accelrys Inc., San Diego, CA.PubMedCrossRefGoogle Scholar
  127. Schuster, D. 2010. 3D pharmacophores as tools for activity profiling. Drug Discovery Today: Technologies 7: e205–e211.CrossRefGoogle Scholar
  128. Segura Campos, M.R., L.A. Chel Guerrero, and D.A. Betancur Ancona. 2010. Angiotensin-I converting enzyme inhibitory and antioxidant activities of peptide fractions extracted by ultrafiltration of cowpea Vigna unguiculata hydrolysates. Journal of the Science of Food and Agriculture 90: 2512–2518.PubMedCrossRefGoogle Scholar
  129. Selnergy, Greenpharma S.A.S, Orléans, France.Google Scholar
  130. Senese, C.L., J. Duca, D. Pan, A.J. Hopfinger, and Y.J. Tseng. 2004. 4D-fingerprints, universal QSAR and QSPR descriptors. Journal of Chemical Information and Computer Sciences 44: 1526–1539.PubMedCrossRefGoogle Scholar
  131. Sepe, V., G. Bifulco, B. Renga, C. D’Amore, S. Fiorucci, and A. Zampella. 2011. Discovery of sulfated sterols from marine invertebrates as a new class of marine natural antagonists of farnesoid-x-receptor. Journal of Medicinal Chemistry 54: 1314–1320.PubMedCrossRefGoogle Scholar
  132. Shibata, T., K. Ishimaru, S. Kawaguchi, H. Yoshikawa, and Y. Hama. 2008. Antioxidant activities of phlorotannins isolated from Japanese Laminariaceae. Journal of Applied Phycology 20: 705–711.CrossRefGoogle Scholar
  133. Shu, Y.Z. 1998. Recent natural products based drug development: A pharmaceutical industry perspective. Journal of Natural Products 61: 1053–1071.PubMedCrossRefGoogle Scholar
  134. Srinivas, E., J.N. Murthy, A.R.R. Rao, and G.N. Sastry. 2006. Recent advances in molecular modeling and medicinal chemistry aspects of phospho-glycoprotein. Current Drug Metabolism 7: 205–217.PubMedCrossRefGoogle Scholar
  135. Stahl, M., and D. Rarey. 2001. Detailed analysis of scoring functions for virtual screening. Journal of Medicinal Chemistry 44: 1035–1042.PubMedCrossRefGoogle Scholar
  136. Stahl, M., W. Guba, and M. Kansy. 2006. Integrating molecular design resources within modern drug discovery research: The Roche experience. Drug Discovery Today 11: 326–333.PubMedCrossRefGoogle Scholar
  137. Steindl, T., and T. Langer. 2005. Docking versus pharmacophore model generation: A comparison of high-throughput virtual screening strategies for the search of human rhinovirus coat protein inhibitors. QSAR & Combinatorial Science 24: 470–479.CrossRefGoogle Scholar
  138. Strohl, W.R. 2000. The role of natural products in a modern drug discovery program. Drug Discovery Today 5: 39–41.PubMedCrossRefGoogle Scholar
  139. Surflex-Dock 2.0, Tripos International, Missouri, USA.Google Scholar
  140. SYBYL 8.0, Tripos International, Missouri, USA.Google Scholar
  141. Taha, M.O., M. Tarairah, H. Zalloum, and G. Abu-Sheikha. 2010. Pharmacophore and QSAR modeling of estrogen receptor beta ligands and subsequent validation and in silico search for new hits. Journal of Molecular Graphics and Modelling 28: 383–400.PubMedCrossRefGoogle Scholar
  142. Tawari, N.R., and M.S. Degani. 2010. Pharmacophore mapping and electronic feature analysis for a series of nitroaromatic compounds with antitubercular activity. Journal of Computational Chemistry 31: 739–751.PubMedGoogle Scholar
  143. Thipnate, P., J. Liu, S. Hannongbua, and A.J. Hopfinger. 2009. 3D pharmacophore mapping using 4D QSAR analysis for the cytotoxicity of lamellarins against human hormone-dependent T47D breast cancer cells. Journal of Chemical Information and Modeling 49: 2312–2322.PubMedCrossRefGoogle Scholar
  144. Thomsen, R., and M.H. Christensen. 2006. MolDock: A new technique for high-accuracy molecular docking. Journal of Medicinal Chemistry 49: 3315–3321.PubMedCrossRefGoogle Scholar
  145. Tierney, M.S., A.K. Croft, and M. Hayes. 2010. A review of antihypertensive and antioxidant activities in macroalgae. Botanica Marina 53: 387–408.CrossRefGoogle Scholar
  146. Trindade-Silva, A.E., G.E. Lim-Fong, K.H. Sharp, and M.G. Haygood. 2010. Bryostatins: Biological context and biotechnological prospects. Current Opinion in Biotechnology 21: 834–842.PubMedCrossRefGoogle Scholar
  147. Trott, O., and A.J. Olson. 2010. Software news and update AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry 31: 455–461.PubMedGoogle Scholar
  148. US Environment Protection Agency, ECOTOX (ECOTOXicology) Database, Release 4.0. 2011. Accessed 3 May 2011.
  149. US Food and Drug Administration, Mold2, Descriptors Generator Software. 2010. US Department of Health and Human Services. Accessed 3 May 2011.
  150. van de Waterbeemd, H., and E. Gifford. 2003. ADMET in silico modelling: Towards prediction paradise? Nature Reviews Drug Discovery 2: 192–204.PubMedCrossRefGoogle Scholar
  151. Vanhuyse, M., J. Kluza, C. Tardy, G. Otero, C. Cuevas, C. Bailly, and A. Lansiaux. 2005. Lamellarin D: A novel pro-apoptotic agent from marine origin insensitive to P-glycoprotein-mediated drug efflux. Cancer Letters 221: 165–175.PubMedCrossRefGoogle Scholar
  152. Verdonk, M.L., J.C. Cole, M.J. Hartshorn, C.W. Murray, and R.D. Taylor. 2003. Improved protein–ligand docking using GOLD. Proteins 52: 609–623.PubMedCrossRefGoogle Scholar
  153. Viennois, E., A.J.C. Pommier, K. Mouzat, A. Oumeddour, F.Z. El Hajjaji, J. Dufour, F. Caira, D.H. Volle, S. Baron, and J.M.A. Lobaccaro. 2011. Targeting liver X receptors in human health: Deadlock or promising trail? Expert Opinion on Therapeutic Targets 15: 219–232.PubMedCrossRefGoogle Scholar
  154. von Korff, M., C. Rufener, M. Stritt, J. Freyss, R. Bar, and T. Sander. 2011. Integration of distributed computing into the drug discovery process. Expert Opinion on Drug Discovery 6: 103–107.CrossRefGoogle Scholar
  155. Wald, C. 2010. Scientists Embrace Openness. Science Careers. doi: 10.1126/science.caredit.a1000036.
  156. Wallach, I., and R. Lilien. 2011. Virtual decoy sets for molecular docking benchmarks. Journal of Chemical Information and Modeling 51: 196–202.PubMedCrossRefGoogle Scholar
  157. Wang, R., and S. Wang. 2001. How does consensus scoring work for virtual library screening? An idealized computer experiment. Journal of Chemical Information and Computer Sciences 41: 1422–1426.PubMedCrossRefGoogle Scholar
  158. Wang, H.B., J. Chen, K. Hollister, L.C. Sowers, and B.M. Forman. 1999. Endogenous bile acids are ligands for the nuclear receptor FXR BAR. Molecular Cell 3: 543–553.PubMedCrossRefGoogle Scholar
  159. Wang, R., Y. Lu, and S. Wang. 2003. Comparative evaluation of 11 scoring functions for molecular docking. Journal of Medicinal Chemistry 46: 2287–2303.PubMedCrossRefGoogle Scholar
  160. Wang, Z., B. Ling, R. Zhang, Y. Suo, Y. Liu, Z. Yu, and C. Liu. 2009. Docking and molecular dynamics studies toward the binding of new natural phenolic marine inhibitors and aldose reductase. Journal of Molecular Graphics and Modelling 28: 162–169.PubMedCrossRefGoogle Scholar
  161. Wang, Z., S. Zhang, W. Wang, F. Feng, and W. Shan. 2011. A novel angiotensin I converting enzyme inhibitory peptide from the milk casein: Virtual screening and docking studies. Agricultural Sciences in China 10: 463–467.CrossRefGoogle Scholar
  162. Warren, G.L., C.W. Andrews, A.M. Capelli, B. Clarke, J. LaLonde, M.H. Lambert, M. Lindvall, N. Nevins, S.F. Semus, S. Senger, G. Tedesco, I.D. Wall, J.M. Woolven, C.E. Peishoff, and M.S. Head. 2006. A critical assessment of docking programs and scoring functions. Journal of Medicinal Chemistry 49: 5912–5931.PubMedCrossRefGoogle Scholar
  163. Watson, P., Leahy, D., Cala, J., Searson, D., Sykora, V., Taylor, M., Woodman, S., Hiden, H., OpenQSAR. 2010. School of Computing Science, Newcastle University., Accessed 3 May 2011.
  164. Welch, W., J. Ruppert, and A.N. Jain. 1996. Hammerhead: Fast, fully automated docking of flexible ligands to protein binding sites. Chemistry and Biology 3: 449–462.PubMedCrossRefGoogle Scholar
  165. Wender, P.A., C.M. Cribbs, K.F. Koehler, N.A. Sharkey, C.L. Herald, Y. Kamano, G.R. Pettit, and P.M. Blumberg. 1988. Modeling of the Bryostatins to the phorbol ester pharmacophore on protein kinase-C. Proceedings of the National Academy of Sciences of the United States of America 85: 7197–7201.PubMedCrossRefGoogle Scholar
  166. Wijesekara, I., and S.K. Kim. 2010. Angiotensin-I-converting enzyme (ACE) inhibitors from marine resources: Prospects in the pharmaceutical industry. Marine Drugs 8: 1080–1093.PubMedCrossRefGoogle Scholar
  167. Williams, A., Chemspider. 2011. Royal Society of Chemistry. Accessed 3 May 2011.
  168. Willighagen, E., Guha, R., Steinbeck, C., Chemistry Development Kit. 2011. Sourceforge. Accessed 3 May 2011.
  169. Wolber, G., and T. Langer. 2005. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. Journal of Chemical Information and Modeling 45: 160–169.PubMedCrossRefGoogle Scholar
  170. World Community Grid, World Community Grid, technology solving problems. 2011. IBM. Accessed 3 May 11.
  171. Wunberg, T., M. Hendrix, A. Hillisch, M. Lobell, H. Meier, C. Schmeck, H. Wild, and B. Hinzen. 2006. Improving the hit-to-lead process: Data-driven assessment of drug-like and lead-like screening hits. Drug Discovery Today 11: 175–180.PubMedCrossRefGoogle Scholar
  172. Yap, T.A., C.P. Carden, and S.B. Kaye. 2009. Beyond chemotherapy: Targeted therapies in ovarian cancer. Nature Reviews Cancer 9: 167–181.PubMedCrossRefGoogle Scholar
  173. Zhou, Z., A.K. Felts, R.A. Friesner, and R.M. Levy. 2007. Comparative performance of several flexible docking programs and scoring functions: Enrichment studies for a diverse set of pharmaceutically relevant targets. Journal of Chemical Information and Modeling 47: 1599–1608.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.School of ChemistryUniversity of Wales BangorBangorUK
  2. 2.Food BioSciences DepartmentTeagasc Food Research CentreDublin 15Ireland

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