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


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).


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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Additional Metadata

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculty of Computer Science and Information Technology
Faculty of Science
DOI Number: https://doi.org/10.1109/ICCST.2014.7045186
Publisher: IEEE (IEEExplore)
Keywords: Evolutionary computation; Reinforcement learning; Q-learning; Flexible docking; Local search; Genetic algorithm
Depositing User: Nursyafinaz Mohd Noh
Date Deposited: 17 Jun 2015 06:51
Last Modified: 08 Jun 2016 02:16
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/ICCST.2014.7045186
URI: http://psasir.upm.edu.my/id/eprint/38800
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