Citation
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).
Abstract
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.
Download File
Full text not available from this repository.
|
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 |
| Statistic Details: | View Download Statistic |
Actions (login required)
 |
View Item |