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Genetic algorithm with a crossover elitist preservation mechanism for protein–ligand docking

Overview of attention for article published in AMB Express, September 2017
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1 tweeter

Citations

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Readers on

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17 Mendeley
Title
Genetic algorithm with a crossover elitist preservation mechanism for protein–ligand docking
Published in
AMB Express, September 2017
DOI 10.1186/s13568-017-0476-0
Pubmed ID
Authors

Boxin Guan, Changsheng Zhang, Jiaxu Ning

Abstract

Protein-ligand docking plays an important role in computer-aided pharmaceutical development. Protein-ligand docking can be defined as a search algorithm with a scoring function, whose aim is to determine the conformation of the ligand and the receptor with the lowest energy. Hence, to improve an efficient algorithm has become a very significant challenge. In this paper, a novel search algorithm based on crossover elitist preservation mechanism (CEP) for solving protein-ligand docking problems is proposed. The proposed algorithm, namely genetic algorithm with crossover elitist preservation (CEPGA), employ the CEP to keep the elite individuals of the last generation and make the crossover more efficient and robust. The performance of CEPGA is tested on sixteen molecular docking complexes from RCSB protein data bank. In comparison with GA, LGA and SODOCK in the aspects of lowest energy and highest accuracy, the results of which indicate that the CEPGA is a reliable and successful method for protein-ligand docking problems.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 3 18%
Student > Doctoral Student 2 12%
Student > Ph. D. Student 2 12%
Other 1 6%
Student > Master 1 6%
Other 2 12%
Unknown 6 35%
Readers by discipline Count As %
Chemistry 3 18%
Biochemistry, Genetics and Molecular Biology 3 18%
Agricultural and Biological Sciences 1 6%
Computer Science 1 6%
Energy 1 6%
Other 1 6%
Unknown 7 41%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 September 2017.
All research outputs
#7,332,039
of 11,767,719 outputs
Outputs from AMB Express
#307
of 705 outputs
Outputs of similar age
#150,999
of 265,036 outputs
Outputs of similar age from AMB Express
#16
of 33 outputs
Altmetric has tracked 11,767,719 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 705 research outputs from this source. They receive a mean Attention Score of 2.2. This one is in the 6th percentile – i.e., 6% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 265,036 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 33 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.