SODOCK: Swarm optimization for highly flexible protein-ligand docking

Hung Ming Chen, Bo Fu Liu, Hui Ling Huang, Shiow Fen Hwang, Shinn-Ying Ho*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

86 Scopus citations


Protein-ligand docking can be formulated as a parameter optimization problem associated with an accurate scoring function, which aims to identify the translation, orientation, and conformation of a docked ligand with the lowest energy. The parameter optimization problem for highly flexible ligands with many rotatable bonds is more difficult than that for less flexible ligands using genetic algorithm (GA)-based approaches, due to the large numbers of parameters and high correlations among these parameters. This investigation presents a novel optimization algorithm SODOCK based on particle swarm optimization (PSO) for solving flexible protein-ligand docking problems. To improve efficiency and robustness of PSO, an efficient local search strategy is incorporated into SODOCK. The implementation of SODOCK adopts the environment and energy function of AutoDock 3.05. Computer simulation results reveal that SODOCK is superior to the Lamarckian genetic algorithm (LGA) of AutoDock, in terms of convergence performance, robustness, and obtained energy, especially for highly flexible ligands. The results also reveal that PSO is more suitable than the conventional GA in dealing with flexible docking problems with high correlations among parameters. This investigation also compared SODOCK with four state-of-the-art docking methods, namely GOLD 1.2, DOCK 4.0, FlexX 1.8, and LGA of AutoDock 3.05. SODOCK obtained the smallest RMSD in 19 of 37 cases. The average 2.29 Å of the 37 RMSD values of SODOCK was better than those of other docking programs, which were all above 3.0 Å.

Original languageEnglish
Pages (from-to)612-623
Number of pages12
JournalJournal of Computational Chemistry
Issue number2
StatePublished - 30 Jan 2007


  • Flexible docking
  • Genetic algorithm
  • Local search
  • Particle swarm optimization
  • Scoring function

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