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- W2014033517 abstract "Since the high computational cost of the structure-based protein-Ligand docking prediction is one of the major problems in designing new drugs, many researchers keep looking for a high performance search algorithm to find the workable directions to drug design as well as a simulator platform being able to test and verify the new drugs. In this paper, an improved version of Lamarckian genetic algorithm (ILGA) is first presented for enhancing the performance of LGA by using pattern reduction to reduce the computation cost and using tabu search to increase the search diversity to further find the better results. In addition, the proposed algorithm is also applied to a well-known simulator platform (AutoDock) to evaluate the performance of the proposed algorithm. The simulation results show that the proposed algorithm can enhance the performance of ILGA in terms of convergence performance especially for highly flexible ligands." @default.
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- W2014033517 date "2012-06-01" @default.
- W2014033517 modified "2023-09-26" @default.
- W2014033517 title "An improved LGA for protein-ligand docking prediction" @default.
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- W2014033517 doi "https://doi.org/10.1109/cec.2012.6256513" @default.
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