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SSGARL: hybrid evolutionary computation and reinforcement learning for flexible ligand docking

This paper presents and investigates the performance of a hybrid algorithm of steady-state genetic algorithm and reinforcement learning (SSGARL) in the problem of protein-ligand docking. The performance was measured in terms of the lowest found docking energy, the number of energy evaluation and the...

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Main Authors: Marlisah, Erzam, Yaakob, Razali, Sulaiman, Md. Nasir, Abdul Rahman, Mohd Basyaruddin
Format: Conference or Workshop Item
Published: IEEE (IEEExplore) 2014
Online Access:http://psasir.upm.edu.my/id/eprint/38800/
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spelling my.upm.eprints.388002016-06-08T02:16:51Z http://psasir.upm.edu.my/id/eprint/38800/ SSGARL: hybrid evolutionary computation and reinforcement learning for flexible ligand docking Marlisah, Erzam Yaakob, Razali Sulaiman, Md. Nasir Abdul Rahman, Mohd Basyaruddin This paper presents and investigates the performance of a hybrid algorithm of steady-state genetic algorithm and reinforcement learning (SSGARL) in the problem of protein-ligand docking. The performance was measured in terms of the lowest found docking energy, the number of energy evaluation and the time taken to complete a docking task. Ten ligands of varying flexibility were chosen to bind with thermolysin to compare the performance of SSGARL and Iterated Local Search global optimizer’s algorithm of AutoDock Vina. The results reveal that SSGARL finds the lowest docking energy, requires lesser number of energy evaluation and is faster in docking the highly flexible ligands. IEEE (IEEExplore) 2014 Conference or Workshop Item NonPeerReviewed Marlisah, Erzam and Yaakob, Razali and Sulaiman, Md. Nasir and Abdul Rahman, Mohd Basyaruddin (2014) SSGARL: hybrid evolutionary computation and reinforcement learning for flexible ligand docking. In: International Conference on Computational Science and Technology (ICCST 2014), 27-28 Aug. 2014, Kota Kinabalu, Sabah. (pp. 1-6). 10.1109/ICCST.2014.7045186
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description This paper presents and investigates the performance of a hybrid algorithm of steady-state genetic algorithm and reinforcement learning (SSGARL) in the problem of protein-ligand docking. The performance was measured in terms of the lowest found docking energy, the number of energy evaluation and the time taken to complete a docking task. Ten ligands of varying flexibility were chosen to bind with thermolysin to compare the performance of SSGARL and Iterated Local Search global optimizer’s algorithm of AutoDock Vina. The results reveal that SSGARL finds the lowest docking energy, requires lesser number of energy evaluation and is faster in docking the highly flexible ligands.
format Conference or Workshop Item
author Marlisah, Erzam
Yaakob, Razali
Sulaiman, Md. Nasir
Abdul Rahman, Mohd Basyaruddin
spellingShingle Marlisah, Erzam
Yaakob, Razali
Sulaiman, Md. Nasir
Abdul Rahman, Mohd Basyaruddin
SSGARL: hybrid evolutionary computation and reinforcement learning for flexible ligand docking
author_facet Marlisah, Erzam
Yaakob, Razali
Sulaiman, Md. Nasir
Abdul Rahman, Mohd Basyaruddin
author_sort Marlisah, Erzam
title SSGARL: hybrid evolutionary computation and reinforcement learning for flexible ligand docking
title_short SSGARL: hybrid evolutionary computation and reinforcement learning for flexible ligand docking
title_full SSGARL: hybrid evolutionary computation and reinforcement learning for flexible ligand docking
title_fullStr SSGARL: hybrid evolutionary computation and reinforcement learning for flexible ligand docking
title_full_unstemmed SSGARL: hybrid evolutionary computation and reinforcement learning for flexible ligand docking
title_sort ssgarl: hybrid evolutionary computation and reinforcement learning for flexible ligand docking
publisher IEEE (IEEExplore)
publishDate 2014
url http://psasir.upm.edu.my/id/eprint/38800/
_version_ 1643832239978446848
score 13.209306

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