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Using High Content Imaging to Quantify Target Engagement in Adherent Cells
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Template CoMFA applied to 116 biological targets.

Richard D Cramer1

  • 1Certara Corporation, 240 N. Tucker Blvd., St. Louis, Missouri 63101, United States.

Journal of Chemical Information and Modeling
|June 10, 2014
PubMed
Summary

Automatic template Comparative Molecular Field Analysis (CoMFA) generated statistically sound 3D-QSAR models for over 95% of 116 biological targets. This approach efficiently models structure-based and ligand-based targets for drug discovery.

Area of Science:

  • Computational Chemistry
  • Medicinal Chemistry
  • Drug Discovery

Background:

  • Quantitative Structure-Activity Relationship (QSAR) modeling is crucial for drug discovery.
  • Developing robust 3D-QSAR models traditionally requires significant expertise and effort.
  • The need for efficient and broadly applicable 3D-QSAR modeling methods is high.

Purpose of the Study:

  • To develop an automated method for generating statistically acceptable 3D-QSAR models.
  • To apply this method to a large set of diverse biological targets.
  • To assess the performance and applicability of automated template CoMFA.

Main Methods:

  • Utilized automatic template Comparative Molecular Field Analysis (CoMFA).
  • Applied the method to 116 biological targets, categorizing them into structure-based (76 targets) and ligand-based (40 targets).

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  • Defined statistical acceptability criteria including leave-one-out q(2) > 0.4, standard error of prediction < 1.0, or r(2)/PLS components ratio > 0.2.

    • Main Results:

    • Achieved statistically acceptable 3D-QSAR models for over 95% of the tested biological targets.
    • Structure-based models effectively integrated multiple templates, while ligand-based models used a single low-energy conformation.
    • Structure-based models enabled direct visual comparison of SAR contours with protein cavity surfaces.

    Conclusions:

    • Automated template CoMFA is a highly efficient and successful approach for generating 3D-QSAR models across a wide range of biological targets.
    • This method significantly streamlines the process of 3D-QSAR model development.
    • The developed models offer valuable insights for structure-based drug design and lead optimization.