Table of Contents
Dataset Papers in Science
Volume 2014 (2014), Article ID 421693, 5 pages
http://dx.doi.org/10.1155/2014/421693
Dataset Paper

DockScreen: A Database of In Silico Biomolecular Interactions to Support Computational Toxicology

1Chemical Computing Group, 1010 Sherbrooke Street W., Suite 910, Montreal, QC, Canada H3A 2R7
2Lockheed Martin Information Technology, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA
3Office of Research & Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA

Received 6 June 2014; Accepted 16 September 2014; Published 11 November 2014

Academic Editor: Josefina Pons

Copyright © 2014 Michael-Rock Goldsmith et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. R. Judson, A. Richard, D. Dix et al., “ACToR—aggregated computational toxicology resource,” Toxicology and Applied Pharmacology, vol. 233, no. 1, pp. 7–13, 2008. View at Publisher · View at Google Scholar · View at Scopus
  2. R. Judson, A. Richard, D. J. Dix et al., “The toxicity data landscape for environmental chemicals,” Environmental Health Perspectives, vol. 117, no. 5, pp. 685–695, 2009. View at Publisher · View at Google Scholar · View at Scopus
  3. J. R. Rabinowitz, M.-R. Goldsmith, S. B. Little, and M. A. Pasquinelli, “Computational molecular modeling for evaluating the toxicity of environmental chemicals: prioritizing bioassay requirements,” Environmental Health Perspectives, vol. 116, no. 5, pp. 573–577, 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. A. M. Richard and C. R. Williams, “Distributed structure-searchable toxicity (DSSTox) public database network: a proposal,” Mutation Research, vol. 499, pp. 27–52, 2002. View at Publisher · View at Google Scholar
  5. M. T. Martin, R. S. Judson, D. M. Reif, R. J. Kavlock, and D. J. Dix, “Profiling chemicals based on chronic toxicity results from the U.S. EPA ToxRef database,” Environmental Health Perspectives, vol. 117, no. 3, pp. 392–399, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. National Research Council, Toxicity Testing in the 21st Century: A Vision and a Strategy, The National Academies Press, Washington, DC, USA, 2007.
  7. D. J. Dix, K. A. Houck, M. T. Martin, A. M. Richard, R. W. Setzer, and R. J. Kavlock, “The toxcast program for prioritizing toxicity testing of environmental chemicals,” Toxicological Sciences, vol. 95, no. 1, pp. 5–12, 2007. View at Publisher · View at Google Scholar · View at Scopus
  8. J. Bajorath, “Integration of virtual and high-throughput screening,” Nature Reviews Drug Discovery, vol. 1, no. 11, pp. 882–894, 2002. View at Publisher · View at Google Scholar · View at Scopus
  9. D. C. Fara, T. I. Oprea, E. R. Prossnitz, C. G. Bologa, B. S. Edwards, and L. A. Sklar, “Integration of virtual and physical screening,” Drug Discovery Today: Technologies, vol. 3, no. 4, pp. 377–385, 2006. View at Publisher · View at Google Scholar · View at Scopus
  10. G. V. Paolini, R. H. Shapland, W. P. van Hoorn, J. S. Mason, and A. L. Hopkins, “Global mapping of pharmacological space,” Nature Biotechnology, vol. 24, no. 7, pp. 805–815, 2006. View at Publisher · View at Google Scholar · View at Scopus
  11. Á. Peragovics, Z. Simon, L. Tombor et al., “Virtual affinity fingerprints for target fishing: a new application of drug profile matching,” Journal of Chemical Information and Modeling, vol. 53, no. 1, pp. 103–113, 2013. View at Publisher · View at Google Scholar · View at Scopus
  12. D. Hristozov, T. I. Oprea, and J. Gasteiger, “Ligand-based virtual screening by novelty detection with self-organizing maps,” Journal of Chemical Information and Modeling, vol. 47, no. 6, pp. 2044–2062, 2007. View at Publisher · View at Google Scholar · View at Scopus
  13. E. J. Matthews, N. L. Kruhlak, R. D. Benz, and J. F. Contrera, “Assessment of the health effects of chemicals in humans: I. QSAR estimation of the maximum recommended therapeutic dose (MRTD) and no effect level (NOEL) of organic chemicals based on clinical trial data,” Current Drug Discovery Technologies, vol. 1, no. 1, pp. 61–76, 2004. View at Publisher · View at Google Scholar · View at Scopus
  14. FLIPPER, “Openeye Scientific Software,” Santa Fe, NM, USA, http://www.eyesopen.com/.
  15. A. J. S. Knox, M. J. Meegan, G. Carta, and D. G. J. Lloyd, “Considerations in compound database preparation “hidden” impact on virtual screening results,” Journal of Chemical Information and Modeling, vol. 45, no. 6, pp. 1908–1919, 2005. View at Publisher · View at Google Scholar
  16. G. G. Briggs, R. K. Freeman, and S. J. Yaffe, Drugs in Pregnancy and Lactation: a Reference Guide to Fetal and Neonatal Risk, Lippincott Williams & Wilkins, 2012.
  17. Chemical Computing Group, Molecular Operating Environment (MOE) v2006.08, Chemical Computing Group, Quebec, Canada, 2006.
  18. T. A. Halgren, “Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94,” Journal of Computational Chemistry, vol. 17, no. 5-6, pp. 490–519, 1996. View at Publisher · View at Google Scholar · View at Scopus
  19. P. Emsley and K. Cowtan, “Coot: model-building tools for molecular graphics,” Acta Crystallographica Section D: Biological Crystallography, vol. 60, pp. 2126–2132, 2004. View at Publisher · View at Google Scholar
  20. eHITS v. 6.1, Simbiosys Inc. Toronto Canada.
  21. Z. Zsoldos, D. Reid, A. Simon, B. S. Sadjad, and A. P. Johnson, “eHiTS: an innovative approach to the docking and scoring function problems,” Current Protein and Peptide Science, vol. 7, no. 5, pp. 421–435, 2006. View at Publisher · View at Google Scholar · View at Scopus
  22. Z. Zsoldos, D. Reid, A. Simon, S. B. Sadjad, and A. P. Johnson, “eHiTS: a new fast, exhaustive flexible ligand docking system,” Journal of Molecular Graphics and Modelling, vol. 26, no. 1, pp. 198–212, 2007. View at Publisher · View at Google Scholar · View at Scopus
  23. D. Plewczynski, M. Łaźniewski, R. Augustyniak, and K. Ginalski, “Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database,” Journal of Computational Chemistry, vol. 32, no. 4, pp. 742–755, 2011. View at Publisher · View at Google Scholar · View at Scopus
  24. Small-Molecule Drug Discovery Suite 2014-1: QikProp , version 3.9, Schrödinger, LLC, New York, NY, USA, 2014.
  25. W. L. Jorgensen, D. S. Maxwell, and J. Tirado-Rives, “Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids,” Journal of the American Chemical Society, vol. 118, no. 45, pp. 11225–11236, 1996. View at Publisher · View at Google Scholar · View at Scopus
  26. M. R. Goldsmith, S. D. Peterson, D. T. Chang et al., “Informing mechanistic toxicology with computational molecular models,” in Computational Toxicology, pp. 139–165, Humana Press, 2012. View at Google Scholar
  27. M. R. Goldsmith, D. T. Chang, J. R. Rabinowitz, S. B. Little, and R. R. Tice, To Hit, or Not to Hit?: In Silico Models of In Vitro Nuclear Receptor Transactivation, National Toxicology Board of Scientific Counselors, 2010.