Evaluating Genetic Algorithms in Protein-Ligand Docking
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.
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