Journal of Computer-Aided Molecular Design

, Volume 20, Issue 10–11, pp 647–671

PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results

  • Steven L. Dixon
  • Alexander M. Smondyrev
  • Eric H. Knoll
  • Shashidhar N. Rao
  • David E. Shaw
  • Richard A. Friesner
Original Paper

Summary

We introduce PHASE, a highly flexible system for common pharmacophore identification and assessment, 3D QSAR model development, and 3D database creation and searching. The primary workflows and tasks supported by PHASE are described, and details of the underlying scientific methodologies are provided. Using results from previously published investigations, PHASE is compared directly to other ligand-based software for its ability to identify target pharmacophores, rationalize structure-activity data, and predict activities of external compounds.

Keywords

Pharmacophore perception 3D QSAR 3D databases Ligand-based design 

References

  1. 1.
    Guner OF (2000) Pharmacophore perception, development, and use in drug design. International University Line, La Jolla, CAGoogle Scholar
  2. 2.
    Van Drie JH (2003) Curr Pharm Design 9:1649CrossRefGoogle Scholar
  3. 3.
    Topliss JG (1983) Quantitative structure-activity relationships of drugs, vol 19. Academic Press, New YorkGoogle Scholar
  4. 4.
    Martin YC (1978) Quantitative drug design: a critical introduction. Marcel Dekker, New YorkGoogle Scholar
  5. 5.
    Hansch C, Fujita T (1964) J Am Chem Soc 86:1616CrossRefGoogle Scholar
  6. 6.
    Gund P, Wipke WT, Langridge R (1974) Computer searching of a molecular structure file for pharmacophoric patterns, vol 3. Elsevier, Amsterdam, pp 33–39Google Scholar
  7. 7.
    Kier LB, Hall LH (1976) Molecular connectivity in chemistry and drug research. Academic Press, LondonGoogle Scholar
  8. 8.
    Hancsh C, Leo A (1979) Substituent constants for correlation analysis in chemistry and biology. Wiley, New YorkGoogle Scholar
  9. 9.
    Hopfinger AJ (1980) J Am Chem Soc 102:7196CrossRefGoogle Scholar
  10. 10.
    Van Drie JH, Weininger D, Martin YC (1989) J Comput-Aided Mol Design 3:225CrossRefGoogle Scholar
  11. 11.
    Lauri G, Bartlett PA (1994) J Comput-Aided Mol Design 8:51CrossRefGoogle Scholar
  12. 12.
    Van Drie JH (1997) J Comput-Aided Mol Design 11:39CrossRefGoogle Scholar
  13. 13.
    Chen X, Rusinko A, III Young SS (1998) J Chem Inf Comput Sci 38:1054CrossRefGoogle Scholar
  14. 14.
    Chen X, Rusinki A, III Tropsha A, Young SS (1999) J Chem Inf Comput Sci 39:887CrossRefGoogle Scholar
  15. 15.
    Greene J, Kahn S, Savoj H, Sprague P, Teig S (1994) J Chem Inf Comput Sci 34:1297CrossRefGoogle Scholar
  16. 16.
    Barnum D, Greene J, Smellie A, Sprague P (1996) J Chem Inf Comput Sci 36:563CrossRefGoogle Scholar
  17. 17.
    Martin YC, In Hansch C, Fujita T (eds) (1995) Classical and 3D QSAR in agrochemistry. American Chemical Society, Washington, DC, pp 318–329Google Scholar
  18. 18.
    Jones G, Willett P, Glen RC (1995) J Comput-Aided Mol Design 9:532CrossRefGoogle Scholar
  19. 19.
    Cramer RD, Patterson DE, Bunce JD (1988) J Am Chem Soc 110:5959CrossRefGoogle Scholar
  20. 20.
    Van Drie JH, In Guner OF (ed) (2000) Pharmacophore perception, development, and use in drug design. International University Line, La Jolla, CA, pp 517–530Google Scholar
  21. 21.
    Ligprep 2.0 (2006) Schrodinger, LLC, New York, NYGoogle Scholar
  22. 22.
    MacroModel 9.1 (2006) Schrodinger, LLC, New York, NYGoogle Scholar
  23. 23.
    Halgren TA (1996) J Comput Chem 17:520CrossRefGoogle Scholar
  24. 24.
    MacroModel 2.0 (2006) User Manual, Schrodinger LLC, New York, NYGoogle Scholar
  25. 25.
    Chang G, Guida W, Still WC (1989) J Am Chem Soc 111:4379CrossRefGoogle Scholar
  26. 26.
    Kolossvary I, Guida WC (1996) J Am Chem Soc 118:5011CrossRefGoogle Scholar
  27. 27.
    SMARTS – Language for Describing Molecular Patterns, Daylight Chemical Information Systems, Inc., Aliso Viejo, CAGoogle Scholar
  28. 28.
    Marshall GR, Barry CD, Bosshard HE, Dammkoehler RA, Dunn DA, In Olson EC, Christoffersen RE (eds) (1979) Computer-assisted drug design. American Chemical Society, Washington, DC, pp 205–226Google Scholar
  29. 29.
    Beusen DD, Marshall GR, In Guner OF (ed) (2000) Pharmacophore perception, development, and use in drug design. International University Line, La Jolla, CA, pp 23–45Google Scholar
  30. 30.
    Van Drie JH (1997) J Chem Inf Comput Sci 37:38CrossRefGoogle Scholar
  31. 31.
    Patel Y, Gillet VJ, Bravi G, Leach AR (2002) J Comput-Aided Mol Design 16:653CrossRefGoogle Scholar
  32. 32.
    Suling WJ, Reynolds RC, Barrow EW, Wilson LN, Piper JR, Barrow WW (1998) J Antimicrob Chemother 42:811CrossRefGoogle Scholar
  33. 33.
    Suling WJ, Seitz LE, Pathak V, Westbrook L, Barrow EW, Zywno-Van-Ginkel S, Reynolds RC, Piper JR, Barrow W (2000) Antimicrob Agents Chemoth 44:2784CrossRefGoogle Scholar
  34. 34.
    Debnath AK (2002) J Med Chem 45:41CrossRefGoogle Scholar
  35. 35.
    Maestro 7.5 (2006) Schrodinger, LLC, New York, NYGoogle Scholar
  36. 36.
    World Drug Index (2001) Thomson ScientificGoogle Scholar
  37. 37.
    Wold H, In Gani J (ed) (1975) Perspectives in probability and statistics, Papers in Honour of Bartlett MS on the Occasion of His Sixty-Fifth Birthday, Academic Press, London, pp 117–142Google Scholar
  38. 38.
    Wold S, Ruhe H, Wold H, Dunn WJI (1984) SIAM J Scientific Stat Comput 5:735CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2006

Authors and Affiliations

  • Steven L. Dixon
    • 1
  • Alexander M. Smondyrev
    • 1
  • Eric H. Knoll
    • 1
    • 2
  • Shashidhar N. Rao
    • 1
  • David E. Shaw
    • 1
    • 3
  • Richard A. Friesner
    • 1
    • 2
  1. 1.Schrödinger, Inc.New YorkUSA
  2. 2.Department of ChemistryColumbia UniversityNew YorkUSA
  3. 3.D E Shaw & CoNew YorkUSA

Personalised recommendations