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Related Experiment Videos

HERG binding specificity and binding site structure: evidence from a fragment-based evolutionary computing SAR study.

William Bains1, Antranig Basman, Cat White

  • 1Amedis Pharmaceuticals, Unit 162 Cambridge Science Park, Milton Road, Cambridge, UK.

Progress in Biophysics and Molecular Biology
|August 4, 2004
PubMed
Summary
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Genetic programming accurately predicts HERG channel blockers using molecular fragments. This evolutionary computing method identifies key structural features for drug discovery, achieving 85-90% accuracy.

Area of Science:

  • Computational Chemistry
  • Pharmacology
  • Bioinformatics

Background:

  • The HERG (human ether-à-go-go-related gene) potassium channel is crucial for cardiac repolarization.
  • Blockade of the HERG channel can lead to fatal cardiac arrhythmias, making its prediction essential in drug safety.
  • Predicting HERG channel inhibition is vital for early-stage drug discovery and development.

Purpose of the Study:

  • To apply genetic programming, an evolutionary computing technique, for predicting HERG channel blockade.
  • To develop predictive models using molecular fragment-based descriptors.
  • To gain structural insights into the pharmacophore responsible for HERG channel interaction.

Main Methods:

  • Utilized genetic programming, a type of evolutionary computation, for predictive modeling.

Related Experiment Videos

  • Employed a molecular fragment-based descriptor set to characterize small molecules.
  • Validated models on a 'blind' set of compounds to assess predictive accuracy.

    • Main Results:

    • Achieved 85-90% accuracy in predicting HERG channel blockade for compounds with IC(50) < 1 microM.
    • Developed a 'meta-SAR' (Structure-Activity Relationship) from the predictive models.
    • Identified a pharmacophore model comprising two hydrophobic features and a protonatable nitrogen.

    Conclusions:

    • Genetic programming offers a robust and efficient method for predicting HERG channel blockers.
    • The identified pharmacophore provides valuable structural understanding for designing safer drug candidates.
    • This approach requires limited scientist input, streamlining the drug discovery process.