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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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 |
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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. |
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Conference or Workshop Item |
author |
Marlisah, Erzam Yaakob, Razali Sulaiman, Md. Nasir Abdul Rahman, Mohd Basyaruddin |
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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/ |
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1643832239978446848 |
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