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.
KeywordsGenetic Algorithm Local Search Energy Function Virtual Screening Random Individual
Unable to display preview. Download preview PDF.
- 4.Schulz-Gasch, T., Stahl, M.: Binding site characteristics in structure-based virtual screening: evaluation of current docking tools. Journal of Molecular Modeling 9(1), 47–57 (2003)Google Scholar
- 6.Wolpert, D.H., Macready, W.G.: No free lunch theorems for imization. IEEE Transactions on Evolutionary Computation (1997)Google Scholar
- 7.Hart, W.E.: Adaptive Global imization with Local Search. PhD thesis, University of California, San Diego (1994)Google Scholar
- 10.Irwin, J.J., Shoichet, B.K.: A free database of commercially available compounds for virtual screening. J. Chem. Inf. Comput. Sci. 45(1), 177–182 (2005)Google Scholar