An Efficient ABC_DE_Based Hybrid Algorithm for Protein–Ligand Docking

Guan, Boxin; Zhao, Yuhai

doi:10.3390/ijms19041181

Cited by 10 publications

(4 citation statements)

References 37 publications

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“…Koohi-Moghadam and Rahmani 24 used an enhanced DE algorithm with an LS algorithm and a pseudo-elitism operator to address the docking problem. Guan et al 25 introduced an efficient ABC_DE-based hybrid algorithm for protein-ligand docking, integrating artificial bee colony algorithm and DE algorithm.…”

Section: Related State-of-the-art Stochastic Optimization Methodsmentioning

confidence: 99%

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Probing molecular docking problem by an improved quantum-behaved particle swarm optimization algorithm

Fu¹,

Martino²,

Zhao³

2019

Journal of Algorithms & Computational Technology

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The main objective of molecular docking is to find a model of interaction between a protein and ligand with a minimum binding energy. This process is driven by intricate algorithms and scoring functions. This paper mainly concentrates on the search algorithm used for solving the docking problem. Here, a new approach is proposed for the molecular docking problem that utilizes a hybrid algorithm that combines an improved quantum-behaved particle swarm optimization algorithm (QPSO) and the Solis and Wets algorithm. The improved QPSO algorithm that is based on individual particle evolutionary processes is known as individual particle evolutionary particle swarm optimization (IEQPSO). The IEQPSO algorithm was tested and compared with particle swarm optimization, QPSO, and its variants with a suite of benchmark functions. The results indicated the superiority of the proposed approach according to benchmark test functions. Then, the hybrid algorithm based on the IEQPSO algorithm was used for optimizing the energy function of the molecular docking problem and was compared with the classical Lamarckian genetic algorithm used by molecular docking software. Molecular docking and molecular dynamics simulation experiments revealed the effectiveness and feasibility of the proposed algorithm in solving the molecular docking problem.

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Section: Related State-of-the-art Stochastic Optimization Methodsmentioning

confidence: 99%

“…Guan et al. 25 introduced an efficient ABC_DE-based hybrid algorithm for protein–ligand docking, integrating artificial bee colony algorithm and DE algorithm.…”

Section: Related State-of-the-art Stochastic Optimization Methodsmentioning

confidence: 99%

Probing molecular docking problem by an improved quantum-behaved particle swarm optimization algorithm

Fu¹,

Martino²,

Zhao³

2019

Journal of Algorithms & Computational Technology

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“…It was revealed that FIPSDock might be more suitable than the conventional GA-based algorithms in dealing with highly flexible docking problems. There are also some other docking algorithms using PSO variants as well as other optimization techniques, such as ant colony optimization (ACO), artificial bee colony (ABC), differential evolution (DE), and fireworks algorithm (FWA) as the search algorithms in AutoDock [22][23][24][25][26][27][28][29][30]. Although all the algorithms mentioned above have good performance in solving protein-ligand docking problems, it is still a challenge task due to the complexity of free energy landscapes and the accuracy and efficiency of docking [31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Entropy‐based lamarckian quantum‐behaved particle swarm optimization for flexible ligand docking

You

Sun

et al. 2023

Molecular Informatics

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AutoDock is a widely used software for flexible ligand docking problems since it is open source and easy to be implemented. In this paper, a novel hybrid algorithm is proposed and applied in the docking environment of AutoDock version 4.2.6 in order to enhance the accuracy and the efficiency for dockings with flexible ligands. This search algorithm, called entropy-based Lamarckian quantum-behaved particle swarm optimization (ELQPSO), is a combination of the QPSO with an entropy-based update strategy and the Solis and Wet local search (SWLS) method. By using the PDBbind core set v.2016, the ELQ-PSO is compared with the Lamarckian genetic algorithm (LGA), Lamarckian particle swarm optimization (LPSO) and Lamarckian QPSO (LQPSO). The experimental results reveal that the corresponding docking program of ELQ-PSO, named as EQDOCK in this paper, has a competitive performance in dealing with the protein-ligand docking problems. Moreover, for the test cases with different number of torsions, the EQDOCK outperforms the other three docking programs in finding docking conformations with small root mean squared deviation (RMSD) values in most cases. In particular, it has an advantage of solving highly flexible ligand docking problems over the others.

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“…A number of variants based on AutoDock or AutoDock Vina were developed, in which the search algorithm is replaced by a metaheuristic algorithm. To name a few, SODOCK [ 8 ], PSO@AUTODOCK [ 9 ], FIPSDock [ 10 ], F l APCps [ 11 ], PSOVina [ 12 , 13 ], and ADHDock [ 14 ] have recently been published and have demonstrated superior performance over the basic AutoDock or AutoDock Vina methods. During the development of PSOVina, we realized that the main weakness of the original particle swarm optimization (PSO) algorithm is insufficient exploration of the search space.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Cuckoo Search and Differential Evolution Approach to Protein–Ligand Docking

Lin¹,

Siu²

2018

IJMS

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Protein–ligand docking is a molecular modeling technique that is used to predict the conformation of a small molecular ligand at the binding pocket of a protein receptor. There are many protein–ligand docking tools, among which AutoDock Vina is the most popular open-source docking software. In recent years, there have been numerous attempts to optimize the search process in AutoDock Vina by means of heuristic optimization methods, such as genetic and particle swarm optimization algorithms. This study, for the first time, explores the use of cuckoo search (CS) to solve the protein–ligand docking problem. The result of this study is CuckooVina, an enhanced conformational search algorithm that hybridizes cuckoo search with differential evolution (DE). Extensive tests using two benchmark datasets, PDBbind 2012 and Astex Diverse set, show that CuckooVina improves the docking performances in terms of RMSD, binding affinity, and success rate compared to Vina though it requires about 9–15% more time to complete a run than Vina. CuckooVina predicts more accurate docking poses with higher binding affinities than PSOVina with similar success rates. CuckooVina’s slower convergence but higher accuracy suggest that it is better able to escape from local energy minima and improves the problem of premature convergence. As a summary, our results assure that the hybrid CS–DE process to continuously generate diverse solutions is a good strategy to maintain the proper balance between global and local exploitation required for the ligand conformational search.

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An Efficient ABC_DE_Based Hybrid Algorithm for Protein–Ligand Docking

Cited by 10 publications

References 37 publications

Probing molecular docking problem by an improved quantum-behaved particle swarm optimization algorithm

Probing molecular docking problem by an improved quantum-behaved particle swarm optimization algorithm

Entropy‐based lamarckian quantum‐behaved particle swarm optimization for flexible ligand docking

A Hybrid Cuckoo Search and Differential Evolution Approach to Protein–Ligand Docking

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