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An Efficient ABC_DE_Based Hybrid Algorithm for Protein-Ligand Docking.

Boxin Guan1, Changsheng Zhang2, Yuhai Zhao3

  • 1School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China. neuguanboxin@163.com.

International Journal of Molecular Sciences
|April 14, 2018
PubMed
Summary
This summary is machine-generated.

A new hybrid algorithm, ADHDOCK, combines artificial bee colony (ABC) and differential evolution (DE) to improve protein-ligand docking efficiency. This method outperforms existing algorithms in finding optimal binding conformations for drug design.

Keywords:
artificial bee colonydifferential evolutiondrug designprotein–ligand docking

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Area of Science:

  • Computational chemistry
  • Bioinformatics
  • Drug discovery

Background:

  • Protein-ligand docking is crucial for drug design, requiring efficient search algorithms.
  • Existing methods face challenges in optimizing binding conformations between receptors and ligands.

Purpose of the Study:

  • To develop a novel hybrid algorithm, ADHDOCK, for enhanced protein-ligand docking.
  • To improve the efficiency and accuracy of automated molecular docking processes.

Main Methods:

  • Integration of Artificial Bee Colony (ABC) and Differential Evolution (DE) algorithms into a hybrid approach (ADHDOCK).
  • Implementation of an adaptive population partition (APP) mechanism for optimized resource allocation.
  • Comparative analysis of ADHDOCK against ABC, DE, LGA, HIGA, and SODOCK using fifty protein-ligand docking problems.

Main Results:

  • ADHDOCK demonstrated superior performance in identifying the lowest binding energy across most tested problems.
  • The hybrid algorithm achieved the smallest root-mean-square deviation (RMSD) in protein-ligand docking.
  • ADHDOCK effectively leveraged the strengths of both ABC and DE algorithms.

Conclusions:

  • ADHDOCK offers a more efficient and accurate solution for protein-ligand docking compared to existing methods.
  • The adaptive population partition mechanism enhances computational resource utilization.
  • This novel algorithm holds significant potential for accelerating drug design and discovery pipelines.