An improved LGA for protein-ligand docking prediction

Chun Wei Tsai, Jui Le Chen, Chu Sing Yang

研究成果: Conference contribution

7 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題2012 IEEE Congress on Evolutionary Computation, CEC 2012
DOIs
出版狀態Published - 2012 十月 4
事件2012 IEEE Congress on Evolutionary Computation, CEC 2012 - Brisbane, QLD, Australia
持續時間: 2012 六月 102012 六月 15

出版系列

名字2012 IEEE Congress on Evolutionary Computation, CEC 2012

Other

Other2012 IEEE Congress on Evolutionary Computation, CEC 2012
國家Australia
城市Brisbane, QLD
期間12-06-1012-06-15

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Theoretical Computer Science

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