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Volume 40, Number 2, 2001
Deep computing for the life sciences
 Table of contents: arrowHTML arrowPDF arrowASCII   This article: arrowHTML arrowPDF arrowASCII arrowCopyright info
   

Large-scale virtual screening for discovering leads in the postgenomic era - References
by B. Waszkowycz, T. D. J. Perkins, R. A. Sykes, and J. Li

Cited references and note

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  4. W. P. Walters, M. T. Stahl, and M. A. Murcko, “Virtual Screening—An Overview,” Drug Discovery Today 3, 160–178 (1998).
  5. DockCrunch website: http://www.protherics.com/crunch.
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  10. T. S. Peat, J. M. Newman, and D. E. Bussiere, “Structural Genomics in the Post-Genomics Era: The Shape of Things to Come,” Current Opinion in Drug Discovery and Development 3, 399–407 (2000).
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  14. D. Tondi, U. Slomczyska, M. P. Costi, et al., “Structure-Based Discovery and In-Parallel Optimization of Novel Competitive Inhibitors of Thymidylate Synthase,” Chemistry and Biology 6, 319–331 (1999).
  15. P. Burkhard, U. Hommel, M. Sanner, and M. D. Walkinshaw, “The Discovery of Steroids and Other Novel FKBP Inhibitors Using a Molecular Docking Program,” Journal of Molecular Biology 287, 853–858 (1999).
  16. M. Schapira, B. M. Raaka, H. H. Samuels, and R. Abagyan, “Rational Discovery of Novel Nuclear Hormone Receptor Antagonists,” Proceedings of the National Academy of Sciences (USA) 97, 1008–1013 (2000).
  17. A. V. Filikov, V. Mohan, T. A. Vickers, et al., “Identification of Ligands for RNA Targets via Structure-Based Virtual Screening,” Journal of Computer-Aided Molecular Design 14, 593–610 (2000).
  18. E. Perola, K. Xu, T. M. Kollmeyer, et al., “Successful Virtual Screening of a Chemical Database for Farnesyltransferase Inhibitor Leads,” Journal of Medicinal Chemistry 43, 401–408 (2000).
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  20. S. C. Hopkins, R. D. Vale, and I. D. Kuntz, “Inhibitors of Kinesin Activity from Structure-Based Computer Screening,” Biochemistry 39, 2805–2814 (2000).
  21. A. M. Aronov, N. R. Munagala, P. R. Ortiz de Montellano, I. D. Kuntz, and C. C. Wang, “Rational Design of Selective Submicromolar Inhibitors of Tritrichmonas foetus Hypoxanthine-Guanine-Xanthine Phosphoribosyltransferase,” Biochemistry 39, 4684–4691 (2000).
  22. C. A. Baxter, C. W. Murray, B. Waszkowycz, et al., “New Approach to Molecular Docking and Its Application to Virtual Screening of Chemical Databases,” Journal of Chemical Information and Computer Science 40, 254–262 (2000).
  23. J. I. MacGregor and C. Jordan, “Basic Guide to the Mechanisms of Antiestrogen Action,” Pharmacological Reviews 50, 152–196 (1998).
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  27. ISIS/Base 2.2, MDL Information Systems, Inc. (MDL), San Leandro, CA.
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  29. M. D. Eldridge, C. W. Murray, T. R. Auton, et al., “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 (1997).
  30. StericPenalty is derived from measuring the contact distance between each point on the van der Waals surface of the ligand and the nearest accessible receptor atom. On subtracting the van der Waals radius of the receptor atom, a value d is obtained that is zero for two atoms separated by the sum of their van der Waals radii, and positive for atoms separated by a greater distance. The median of the set of d values for a given ligand was found to be a useful measure of the receptor–ligand steric complementarity. As this median value was found to be correlated with ligand size, the value is normalized by ligand surface area with respect to the set of receptor–ligand complexes used for the calibration of the ChemScore scoring function. The normalized value, StericPenalty, has a value of zero for ligands as tightly bound as the average of the reference set, has a negative value for ligands more tightly bound (e.g., clashing), and a positive value for ligands less tightly bound.
  31. J. D. Oburn, N. J. Koszewski, and A. C. Notides, “Hormone- and DNA-Binding Mechanisms of the Recombinant Human Estrogen Receptor,” Biochemistry 32, 6229–6236 (1993).
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