Skip to main content
SpringerLink
Log in

QSID Tool: a new three-dimensional QSAR environmental tool

  • Published:
Journal of Computer-Aided Molecular Design Aims and scope Submit manuscript

Abstract

QSID Tool (Quantitative structure–activity relationship tool for Innovative Discovery) was developed to provide an easy-to-use, robust and high quality environmental tool for 3D QSAR. Predictive models developed with QSID Tool can accelerate the discovery of lead compounds by enabling researchers to formulate and test hypotheses for optimizing efficacy and increasing drug safety and bioavailability early in the process of drug discovery. QSID Tool was evaluated by comparison with SYBYL® using two different datasets derived from the inhibitors of Trypsin (Böhm et al., J Med Chem 42:458, 1999) and p38-MAPK (Liverton et al., J Med Chem 42:2180, 1999; Romeiro et al., J Comput Aided Mol Des 19:385, 2005; Romeiro et al., J Mol Model 12:855, 2006). The results suggest that QSID Tool is a useful model for the prediction of new analogue activities.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Source 1
Source 2
Fig. 1
Fig. 2
Fig. 3
Fig 4

Similar content being viewed by others

References

  1. Hansch C, Klein T (1986) Acc Chem Res 19:392

    Article  CAS  Google Scholar 

  2. Virtual Computational Chemistry Laboratory http://www.vcclab.org. Accessed Feb. 2008

  3. Cheminformatics Modeling Laboratory http://eccr.stat.ncsu.edu/ChemModLab/. Accessed Feb. 2008

  4. Hicklin J, Moler C, Webb P, Boisvert R, Miller B, Pozo R, Remington K Jama: a Java matrix package. http://math.nist.gov/javanumerics/jama. Accessed Nov. 2007

  5. Steinbeck C, Han Y, Kuhn S, Horlacher O, Luttmann E, Willighagen E (2003) J Chem Inf Comput Sci (JCICS) 43:1077

    Google Scholar 

  6. Paolo Marrone Joone: Java object oriented neural engine. http://www.jooneworld.com/. Accessed Jan. 2008

  7. The Open Source Chemistry Toolbox http://openbabel.sourceforge.net. Accessed Nov. 2007

  8. Gasteiger J, Marsili M (1978) Tetrahedron Lett 34:3181

    Article  Google Scholar 

  9. Gasteiger J, Marsili M (1980) Tetrahedron 36:3219

    Article  CAS  Google Scholar 

  10. JOELib: A Java based cheminformatics library http://www-ra.informatik.uni-tuebingen.de/software/joelib/. Accessed Feb. 2008

  11. Masuda T, Jikihara T, Nakamura K, Kimura A, Takagi T, Fujiwara H (2000) J Pharm Sci 86:57

    Article  Google Scholar 

  12. Shrake A, Rupley JA (1973) J Mol Biol 79:351

    Article  CAS  Google Scholar 

  13. Le Grand S, Merz K (1993) J Comp Chem 14:349

    Article  CAS  Google Scholar 

  14. Lennard-Jones JE (1931) Cohesion. Proc Phys Soc 43:461

    Article  CAS  Google Scholar 

  15. Wang R, Liu L, Lai L, Tang Y (1998) J Mol Model 4:379

    Article  CAS  Google Scholar 

  16. Clark M, Cramer RD III (1993) Quant Struct Act Relat 12:137

    Article  CAS  Google Scholar 

  17. Ian H, Eibe F (2005) Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco

    Google Scholar 

  18. Herve Abdi, LEAST Squares (PLS) Regression, The University of Texas at Dallas

  19. Hoskuldsson A (1988) J Chemometr 2:211

    Article  Google Scholar 

  20. http://en.wikipedia.org/wiki/Neural_network. Accessed Nov. 2007

  21. Werbos P (1974) Beyond regression: new tools for prediction and analysis in the behavioural science. PhD dissertation, Committee on Application Mathematics, Harvard University, Cambridge, MA

  22. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation, Parallel distributed processing. MIT Press, Cambridge

    Google Scholar 

  23. Wikel JH, Dow ER, Heathman M Interpretative neural networks for QSAR. http://www.netsci.org/Science/Compchem/feature02.html. Accessed Nov. 2007

  24. Tetko IV, Livingstone DJ, Luik AI (1995) J Chem Inf Comput Sci 35:826

    CAS  Google Scholar 

  25. Gorman RP, Sejnowski TJ (1988) Neural Nets 1:75

    Article  Google Scholar 

  26. Böhm M, Stürzebecher J, Klebe G (1999) J Med Chem 42:458

    Article  Google Scholar 

  27. Liverton NJ, Butcher JW, Claiborne CF, Claremon DA, Libby BE, Nguyen KT, Pitzenberger SM, Selnick HG, Smith GR, Tebben A, Vacca JP, Varga SL, Agarwal L, Dancheck K, Forsyth AJ, Fletcher DS, Frantz B, Hanlon WA, Harper CF, Hofsess SJ, Kostura M, Lin J, Luell S, O’Neill EA, O’Keefe SJ (1999) J Med Chem 42:2180

    Article  CAS  Google Scholar 

  28. Romeiro NC, Albuquerque MG, de Alencastro RB, Ravi M, Hopfinger AJ (2005) J Comput Aided Mol Des 19:385

    Article  CAS  Google Scholar 

  29. Romeiro NC, Albuquerque MG, de Alencastro RB, Ravi M, Hopfinger AJ (2006) J Mol Model 12:855

    Article  CAS  Google Scholar 

  30. Molecular Networks http://www.molecular-networks.com/software/corina. Accessed Nov. 2007

  31. A language for describing molecular Patterns http://www.daylight.com/dayhtml/doc/theory/theory.smarts.html. Accessed Nov. 2007

Download references

Acknowledgments

This work is part of an internal project supported by Rexahn Pharmaceutical, Inc. The authors extend their appreciation to Drs. John Orban, Edith C. Wolff, James Song and Yonil Park for discussion and critical reviews.

Author information

Authors and Affiliations

  1. Rexahn Pharmaceuticals, Inc., 9620 Medical Center Dr., Rockville, MD, 20850, USA

    Dong Sun Park, Jae Min Kim, Young Bok Lee & Chang Ho Ahn

Authors
  1. Dong Sun Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jae Min Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Young Bok Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chang Ho Ahn
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dong Sun Park.

Electronic supplementary material

Below is the link to the electronic supplementary material.

(DOC 32575 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Park, D.S., Kim, J.M., Lee, Y.B. et al. QSID Tool: a new three-dimensional QSAR environmental tool. J Comput Aided Mol Des 22, 873–883 (2008). https://doi.org/10.1007/s10822-008-9219-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-008-9219-2

Keywords

Search

Navigation