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HIGA: A Running History Information Guided Genetic Algorithm for Protein-Ligand Docking
Boxin Guan1, Changsheng Zhang2, Yuhai Zhao3
1Key Laboratory of Medical Image Computing of Northeastern University, Ministry of Education, and School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China. 13940589067@sina.cn.
Molecules (Basel, Switzerland)
|December 16, 2017
Summary
This study introduces HIGA, a novel algorithm for protein-ligand docking that improves upon Lamarckian genetic algorithm (LGA) by incorporating historical data. HIGA efficiently identifies optimal binding patterns, outperforming existing search algorithms.
Area of Science:
- Computational chemistry
- Bioinformatics
- Drug discovery
Background:
- Protein-ligand docking is crucial for computer-aided drug design, simulating binding patterns.
- Existing methods like Lamarckian genetic algorithm (LGA) lack memory of past solutions, hindering efficiency.
- Discovering optimal protein-ligand binding configurations is computationally intensive.
Purpose of the Study:
- To introduce a novel optimization algorithm, HIGA, designed to enhance protein-ligand docking.
- To address the limitations of LGA by incorporating a history-guided model.
- To improve the efficiency and accuracy of identifying low-energy protein-ligand complexes.
Main Methods:
- Development of HIGA, a hybrid algorithm based on LGA.
- Integration of a running history information guided model, including CE crossover and ED mutation.
- Utilizing a BSP tree structure within the HIGA framework.
Main Results:
- HIGA demonstrates superior performance in protein-ligand docking compared to GA, LGA, EDGA, CEPGA, SODOCK, and ABC.
- The history-guided model significantly improves the efficiency of finding the lowest energy states.
- HIGA effectively overcomes the memory limitation of traditional LGA.
Conclusions:
- HIGA represents a significant advancement in protein-ligand docking algorithms.
- The incorporation of historical data is key to HIGA's enhanced efficiency and performance.
- This novel algorithm offers a more effective approach for drug design simulations.
