These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: An Efficient ABC_DE_Based Hybrid Algorithm for Protein-Ligand Docking.
    Author: Guan B, Zhang C, Zhao Y.
    Journal: Int J Mol Sci; 2018 Apr 13; 19(4):. PubMed ID: 29652791.
    Abstract:
    Protein–ligand docking is a process of searching for the optimal binding conformation between the receptor and the ligand. Automated docking plays an important role in drug design, and an efficient search algorithm is needed to tackle the docking problem. To tackle the protein–ligand docking problem more efficiently, An ABC_DE_based hybrid algorithm (ADHDOCK), integrating artificial bee colony (ABC) algorithm and differential evolution (DE) algorithm, is proposed in the article. ADHDOCK applies an adaptive population partition (APP) mechanism to reasonably allocate the computational resources of the population in each iteration process, which helps the novel method make better use of the advantages of ABC and DE. The experiment tested fifty protein–ligand docking problems to compare the performance of ADHDOCK, ABC, DE, Lamarckian genetic algorithm (LGA), running history information guided genetic algorithm (HIGA), and swarm optimization for highly flexible protein–ligand docking (SODOCK). The results clearly exhibit the capability of ADHDOCK toward finding the lowest energy and the smallest root-mean-square deviation (RMSD) on most of the protein–ligand docking problems with respect to the other five algorithms.
    [Abstract] [Full Text] [Related] [New Search]