Evaluating Genetic Algorithms in Protein-Ligand Docking

  • Rafael Ördög
  • Vince Grolmusz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4983)


In silico protein-ligand docking is a basic problem in pharmaceutics and bio-informatics research. One of the very few protein-ligand docking software with available source is the Autodock 3.05 of the Scripps Research Institute. Autodock 3.05 uses a Lamarckian genetic algorithm for global optimization with a Solis-Wets local search strategy. In this work we evaluate the convergence speed and the deviation properties of the solution produced by Autodock with diverse parameter settings. We conclude that the docking energies found by the genetic algorithm have uncomfortably large deviations. We also suggest a method for considerably decreasing the deviation while the number of evaluations will not be increased.


Genetic Algorithm Local Search Energy Function Virtual Screening Random Individual 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Rafael Ördög
    • 1
  • Vince Grolmusz
    • 1
  1. 1.Protein Information Technology GroupEötvös University, Budapest and, Uratim Ltd.NyíregyházaHungary

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