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Open AccessArticle

An Efficient ABC_DE_Based Hybrid Algorithm for Protein–Ligand Docking

School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2018, 19(4), 1181;
Received: 14 March 2018 / Revised: 7 April 2018 / Accepted: 10 April 2018 / Published: 13 April 2018
(This article belongs to the Section Molecular Biophysics)
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. View Full-Text
Keywords: drug design; protein–ligand docking; artificial bee colony; differential evolution drug design; protein–ligand docking; artificial bee colony; differential evolution
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Guan, B.; Zhang, C.; Zhao, Y. An Efficient ABC_DE_Based Hybrid Algorithm for Protein–Ligand Docking. Int. J. Mol. Sci. 2018, 19, 1181.

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