ArticleJournal of computational biology : a journal of computational molecular cell biology2016
EDGA: A Population Evolution Direction-Guided Genetic Algorithm for Protein-Ligand Docking.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Computational Design of Drugs for Epilepsy using a Novel Guided Evolutionary Algorithm for Enhanced Blood Brain Barrier Permeability.Central nervous system agents in medicinal chemistry · 2026Article
- Malonyl-CoA is a conserved endogenous ATP-competitive mTORC1 inhibitor.Nature cell biology · 2023Article
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- Design and synthesis of some novel triazine-tyrosine hybrids as potential agents for the treatment of multiple sclerosis.Research in pharmaceutical sciences · 2022Article
- Monastrol derivatives:Research in pharmaceutical sciences · 2020Article
- HIGA: A Running History Information Guided Genetic Algorithm for Protein-Ligand Docking.Molecules (Basel, Switzerland) · 2017Article
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Abstract
Protein-ligand docking can be formulated as a search algorithm associated with an accurate scoring function. However, most current search algorithms cannot show good performance in docking problems, especially for highly flexible docking. To overcome this drawback, this article presents a novel and robust optimization algorithm (EDGA) based on the Lamarckian genetic algorithm (LGA) for solving flexible protein-ligand docking problems. This method applies a population evolution direction-guided model of genetics, in which search direction evolves to the optimum solution. The method is more efficient to find the lowest energy of protein-ligand docking. We consider four search methods-a tradition genetic algorithm, LGA, SODOCK, and EDGA-and compare their performance in docking of six protein-ligand docking problems. The results show that EDGA is the most stable, reliable, and successful.
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