Flexible ligand docking using evolutionary algorithms: investigating the effects of variation operators and local search hybrids

The docking of ligands to proteins can be formulated as a computational problem where the task is to find the most favorable energetic conformation among the large space of possible protein–ligand complexes. Stochastic search methods such as evolutionary algorithms (EAs) can be used to sample large search spaces effectively and is one of the commonly used methods for flexible ligand docking. During the last decade, several EAs using different variation operators have been introduced, such as the ones provided with the AutoDock program. In this paper we evaluate the performance of different EA settings such as choice of variation operators, population size, and usage of local search. The comparison is performed on a suite of six docking problems previously used to evaluate the performance of search algorithms provided with the AutoDock program package. The results from our investigation confirm that the choice of variation operators has an impact on the search-capabilities of EAs. The introduced DockEA using the best settings found obtained the overall best docking solutions compared to the Lamarckian GA (LGA) provided with AutoDock. Furthermore, the DockEA proved to be more robust than the LGA (in terms of reproducing the results in several runs) on the more difficult problems with a high number of flexible torsion angles.