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POEM: Parameter Optimization using Ensemble Methods: application to target specific scoring functions.

Iris Antes1, Christian Merkwirth, Thomas Lengauer

  • 1Max-Planck-Institut für Informatik, Stuhlsatzenhausweg 85, D-66123 Saarbrücken, Germany. antes@mpi-sb.mpg.de

Journal of Chemical Information and Modeling
|September 27, 2005
PubMed
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A new method, POEM (Parameter Optimization using Ensemble Methods), optimizes computational biology models by combining Design of Experiments with regression ensembles. This approach efficiently improves scoring functions for molecular docking, outperforming original parameter sets.

Area of Science:

  • Computational biology
  • Biophysics
  • Bioinformatics

Background:

  • Empirical models are crucial for describing processes like molecular docking, binding, and folding in computational biology.
  • Model quality heavily relies on the appropriate selection of adjustable parameters, which is often a complex optimization challenge.
  • Existing methods struggle with the complexity and ruggedness of parameter optimization landscapes.

Purpose of the Study:

  • To introduce POEM (Parameter Optimization using Ensemble Methods), a novel computational method for optimizing parameters in empirical models.
  • To enhance the accuracy and performance of scoring functions used in molecular docking applications.
  • To provide an efficient and robust solution for parameter optimization, even on complex, rugged landscapes.

Main Methods:

Related Experiment Videos

  • POEM combines Design of Experiments (DOE) procedures with ensembles of diverse regression methods.
  • It employs an iterative cycle of evaluation and prediction steps to refine parameter optimization.
  • An approximate function is fitted to the loss function landscape, with data augmentation improving the fit over cycles.

Main Results:

  • The POEM method was successfully applied to optimize FlexX and Screenscore scoring functions for kinase and ATPase protein classes.
  • Starting from random parameters, POEM located parameter sets demonstrating superior performance compared to original values.
  • The optimization process converged rapidly, and the approximated loss function landscapes were smooth.

Conclusions:

  • POEM offers a powerful and efficient approach for optimizing parameters in computational biology models, particularly for scoring functions in molecular docking.
  • The method's ability to handle rugged landscapes and its quick convergence make it suitable for complex optimization tasks.
  • POEM demonstrates significant potential for improving the predictive power and accuracy of empirical models in bioinformatics and related fields.