PARTICLE SWARM OPTIMIZATION ON FLEXIBLE DOCKING

Abstract

Molecular docking is an important tool in screening large libraries of compounds to determine the interactions between potential drugs and the target proteins. The molecular 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 semiempirical free energy force field in AutoDock 4.05. The search ability and the docking accuracy 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 variant which has promising prospects that can be expected in the application to virtual screening.