Evaluating the use of local search strategies for a memetic algorithm for the protein-ligand docking problem

Ruiz-Tagle, Benjamin; Villalobos-Cid, Manuel; Dorn, Márcio; Inostroza-Ponta, Mario

doi:10.1109/sccc.2017.8405141

Cited by 4 publications

(3 citation statements)

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“…To predict the correct conformation of ligands, search algorithms adopt various techniques, such as checking the chemistry and geometry of the atoms involved [DOCK 6 (Allen et al, 2015), FLEXX (Rarey et al, 1996)], genetic algorithm [GOLD (Verdonk et al, 2003)] and incremental construction (Friesner et al, 2004). Algorithms that consider ligand flexibility can be divided into three types: systematic, stochastic and deterministic (Ruiz-Tagle et al, 2018). Some software uses more than one of these approaches to obtain better results.…”

Section: Search Algorithmsmentioning

confidence: 99%

“…Stochastic search algorithms perform random changes in the spatial conformation of the ligand, usually changing one system degree of freedom at a time, which leads to the exploration of several possible conformations (Ruiz-Tagle et al, 2018). The main problem of stochastic algorithms is the uncertainty of converging to a good solution.…”

Section: Search Algorithmsmentioning

confidence: 99%

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Structure-Based Virtual Screening: From Classical to Artificial Intelligence

et al. 2020

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Section: Search Algorithmsmentioning

confidence: 99%

Section: Search Algorithmsmentioning

confidence: 99%

Structure-Based Virtual Screening: From Classical to Artificial Intelligence

et al. 2020

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“…To predict the correct conformation of ligands, search algorithms adopt various techniques, such as checking the chemistry and geometry of the atoms involved [DOCK 6 (Allen et al, 2015), FLEXX (Rarey et al, 1996)], genetic algorithm [GOLD ] and incremental construction . Algorithms that consider ligand flexibility can be divided into three types: systematic, stochastic and deterministic (Ruiz-Tagle et al, 2018). Some software uses more than one of these approaches to obtain better results.…”

Section: Search Algorithmsmentioning

confidence: 99%

In Silico Methods for Drug Design and Discovery

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Kuča³

et al. 2020

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A Biased Random Key Genetic Algorithm with Local Search Chains for Molecular Docking

Leonhart

Dorn

2019

Applications of Evolutionary Computation

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Evaluating the use of local search strategies for a memetic algorithm for the protein-ligand docking problem

Cited by 4 publications

References 17 publications

Structure-Based Virtual Screening: From Classical to Artificial Intelligence

Structure-Based Virtual Screening: From Classical to Artificial Intelligence

In Silico Methods for Drug Design and Discovery

A Biased Random Key Genetic Algorithm with Local Search Chains for Molecular Docking

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