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Related Experiment Video

Updated: Jun 28, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
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A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Virtual screening for R-groups, including predicted pIC50 contributions, within large structural databases, using

Richard D Cramer1, Phillip Cruz, Gunther Stahl

  • 1Tripos International, 1699 South Hanley Road, St. Louis, Missouri 63144, USA. cramer@tripos.com

Journal of Chemical Information and Modeling
|October 30, 2008
PubMed
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This study introduces an objective method for R-group searching in drug discovery, improving pIC50 predictions and enabling efficient virtual screening of chemical databases for novel drug candidates.

Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Drug Discovery

Background:

  • R-group searching in large structural databases is desirable for drug discovery but currently impractical.
  • Existing 3D-QSAR (quantitative structure-activity relationship) approaches for pIC50 prediction are subjective and difficult.
  • Objective methods are needed to facilitate R-group analysis and enhance virtual screening.

Purpose of the Study:

  • To develop an objective and practical method for R-group searching and pIC50 contribution forecasting.
  • To evaluate the performance of topomer-based 3D-QSAR models in predicting R-group activity.
  • To assess the utility of virtual screening using these models for identifying potent R-groups.

Main Methods:

  • Utilized objectively generated topomer poses to replace manual alignments in 3D-QSAR modeling.

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Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source
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Last Updated: Jun 28, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
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Published on: November 3, 2011

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source
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  • Developed a novel leave-one-R-group-out (LOORG) protocol for rigorous prediction accuracy testing.
  • Employed Receiver Operating Curve (ROC) analysis to evaluate virtual screening performance in identifying highly active R-groups.
  • Applied topomer-CoMFA models to screen a large chemical database (ZINC) for R-group candidates.

    • Main Results:

    • Topomer-based 3D-QSAR models achieved statistical quality comparable to published manual alignment models with minimal effort.
    • The LOORG protocol yielded an average pIC50 prediction error of 0.805 and an average predictive r(2) of 0.495.
    • ROC analysis showed an average area of 0.729, indicating a 3-to-1 odds of identifying highly active R-groups.
    • Virtual screening identified an average of 5705 R-groups per search, with top candidates showing significantly higher predicted pIC50 values.

    Conclusions:

    • Objective topomer poses provide a practical and effective alternative to manual alignments for 3D-QSAR modeling.
    • Topomer-CoMFA enables accurate pIC50 prediction and successful virtual screening for potent R-groups.
    • This approach significantly enhances the efficiency and scope of R-group searching in drug discovery.