Molecular docking is an important tool in screening large libraries of compounds to determine the interactions between potential drugs and the target proteins. The molec- ular docking problem is how to locate a good conformation to dock a ligand to the large molecule. It can be formulated as a parameter optimization problem consisting of a scoring function and a global optimization method. Many docking methods have been developed with primarily these two parts varying. In this paper, a variety of particle swarm optimization （PSO） variants were introduced to cooperate with the semiempir- ical free energy force field in AutoDock 4.05. The search ability and the docking accu- racy of these methods were evaluated by multiple redocking experiments. The results demonstrate that PSOs were more suitable than Lamarckian genetic algorithm （LGA）. Among all of the PSO variants, FIPS takes precedence over others. Compared with the four state-of-art docking methods-GOLD, DOCK, FlexX and AutoDock with LGA, AutoDock cooperated with FIPS is more accurate. Thus, FIPS is an efficient PSO vari- ant which has promising prospects that can be expected in the application to virtual screening.
Molecular docking method plays an important role on the quest of potential drug candidates, which has been proven to be a valuable tool for virtual screening. Molecular docking is commonly referred to as a parameter optimization problem. During the last decade, some optimization algorithms have been introduced, such as Lamarckian genetic algorithm （LGA） and SODOCK embedded in the AutoDock program. On the basis of the latest docking software AutoDock4.2, we present a novel docking program ABCDock, which incorporates mutual artificial bee colony （MutualABC） into AutoDock. Computer simulation results demonstrate that ABCDock takes precedence over AutoDock and SODOCK, in terms of convergence performance, accuracy, and the lowest energy, especially for highly flexible ligands. It is noteworthy that ARCDock yields a higher success rate. Also, in comparison with the other state-of-the-art docking methods, namely GOLD, DOCK and FlexX, ABCDock provides the smallest RMSD in 27 of 37 cases.
The recently invented artificial bee colony （ABC） al- gorithm is an optimization algorithm based on swarm intelligence that has been used to solve many kinds of numerical function optimization problems. It performs well in most cases, however, there still exists an insufficiency in the ABC algorithm that ignores the fitness of related pairs of individuals in the mechanism of find- ing a neighboring food source. This paper presents an improved ABC algorithm with mutual learning （MutualABC） that adjusts the produced candidate food source with the higher fitness between two individuals selected by a mutual learning factor. The perfor- mance of the improved MutualABC algorithm is tested on a set of benchmark functions and compared with the basic ABC algo- rithm and some classical versions of improved ABC algorithms. The experimental results show that the MutualABC algorithm with appropriate parameters outperforms other ABC algorithms in most experiments.
