Journal of Computational Chemistry · 2010 · 287 citations · 30 references
PharmacyChemistryMedicinal ChemistryComputational ChemistryDrug DesignDrug DiscoveryComputational Drug DiscoveryEvolutionary OptimizationGenetic AlgorithmsLigand BindingMolecular DockingProtein-ligand DockingFlexible DockingFlexible Ligand DockingGenetic Algorithm OptimizationHeuristicsLigand FlexibilityLigand-protein DockingLocal OptimizationSearch Heuristics
We present a Lamarckian genetic algorithm (LGA) variant for flexible ligand-receptor docking which allows to handle a large number of degrees of freedom. Our hybrid method combines a multi-deme LGA with a recently published gradient-based method for local optimization of molecular complexes. We compared the performance of our new hybrid method to two non gradient-based search heuristics on the Astex diverse set for flexible ligand-receptor docking. Our results show that the novel approach is clearly superior to other LGAs employing a stochastic optimization method. The new algorithm features a shorter run time and gives substantially better results, especially with increasing complexity of the ligands. Thus, it may be used to dock ligands with many rotatable bonds with high efficiency.
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R: A Language and Environment for Statistical Computing
R Core Team · 2000 · 353K citations · Full text
Natural Sciences, Computer Science, Statistical Computing +10
A Simplex Method for Function Minimization
J. A. Nelder, R. Mead · The Computer Journal · 1965 · 28.9K citations