title: EDGA: A Population Evolution Direction-Guided Genetic Algorithm for Protein-Ligand Docking.
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.
authors.affiliation: College of Information Science & Engineering, Northeastern University , Shenyang, People's Republic of China .
authors.forename: Boxin
authors.initials: B
authors.lastname: Guan
authors.affiliation: College of Information Science & Engineering, Northeastern University , Shenyang, People's Republic of China .
authors.forename: Changsheng
authors.initials: C
authors.lastname: Zhang
authors.affiliation: College of Information Science & Engineering, Northeastern University , Shenyang, People's Republic of China .
authors.forename: Jiaxu
authors.initials: J
authors.lastname: Ning
mesh_terms: D000465:Algorithms; D019143:Evolution, Molecular; D008024:Ligands; D062105:Molecular Docking Simulation / Q000379:methods*; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*
chemical_list: D008024:Ligands; D011506:Proteins
keywords: automated docking; drug design; evolutionary direction; genetic algorithm; protein–ligand docking