new Lamarckian genetic algorithm for flexible ligand-receptor docking
We present a Lamarckian genetic algorithm (LGA) variant for flexible ligand-receptor docking which allows to handle a large number of degrees of freedom. Our hybrid method combines a multi-deme LGA with a recently published gradient-based method for local optimization of molecular complexes. We comp...
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| Vydané v: | Journal of computational chemistry Ročník 31; číslo 9; s. 1911 - 1918 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Hoboken
Wiley Subscription Services, Inc., A Wiley Company
15.07.2010
Wiley Subscription Services, Inc |
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| ISSN: | 0192-8651, 1096-987X, 1096-987X |
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| Abstract | We present a Lamarckian genetic algorithm (LGA) variant for flexible ligand-receptor docking which allows to handle a large number of degrees of freedom. Our hybrid method combines a multi-deme LGA with a recently published gradient-based method for local optimization of molecular complexes. We compared the performance of our new hybrid method to two non gradient-based search heuristics on the Astex diverse set for flexible ligand-receptor docking. Our results show that the novel approach is clearly superior to other LGAs employing a stochastic optimization method. The new algorithm features a shorter run time and gives substantially better results, especially with increasing complexity of the ligands. Thus, it may be used to dock ligands with many rotatable bonds with high efficiency. |
|---|---|
| AbstractList | We present a Lamarckian genetic algorithm (LGA) variant for flexible ligand-receptor docking which allows to handle a large number of degrees of freedom. Our hybrid method combines a multi-deme LGA with a recently published gradient-based method for local optimization of molecular complexes. We compared the performance of our new hybrid method to two non gradient-based search heuristics on the Astex diverse set for flexible ligand-receptor docking. Our results show that the novel approach is clearly superior to other LGAs employing a stochastic optimization method. The new algorithm features a shorter run time and gives substantially better results, especially with increasing complexity of the ligands. Thus, it may be used to dock ligands with many rotatable bonds with high efficiency.We present a Lamarckian genetic algorithm (LGA) variant for flexible ligand-receptor docking which allows to handle a large number of degrees of freedom. Our hybrid method combines a multi-deme LGA with a recently published gradient-based method for local optimization of molecular complexes. We compared the performance of our new hybrid method to two non gradient-based search heuristics on the Astex diverse set for flexible ligand-receptor docking. Our results show that the novel approach is clearly superior to other LGAs employing a stochastic optimization method. The new algorithm features a shorter run time and gives substantially better results, especially with increasing complexity of the ligands. Thus, it may be used to dock ligands with many rotatable bonds with high efficiency. We present a Lamarckian genetic algorithm (LGA) variant for flexible ligand‐receptor docking which allows to handle a large number of degrees of freedom. Our hybrid method combines a multi‐deme LGA with a recently published gradient‐based method for local optimization of molecular complexes. We compared the performance of our new hybrid method to two non gradient‐based search heuristics on the Astex diverse set for flexible ligand‐receptor docking. Our results show that the novel approach is clearly superior to other LGAs employing a stochastic optimization method. The new algorithm features a shorter run time and gives substantially better results, especially with increasing complexity of the ligands. Thus, it may be used to dock ligands with many rotatable bonds with high efficiency. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010 We present a Lamarckian genetic algorithm (LGA) variant for flexible ligand-receptor docking which allows to handle a large number of degrees of freedom. Our hybrid method combines a multi-deme LGA with a recently published gradient-based method for local optimization of molecular complexes. We compared the performance of our new hybrid method to two non gradient-based search heuristics on the Astex diverse set for flexible ligand-receptor docking. Our results show that the novel approach is clearly superior to other LGAs employing a stochastic optimization method. The new algorithm features a shorter run time and gives substantially better results, especially with increasing complexity of the ligands. Thus, it may be used to dock ligands with many rotatable bonds with high efficiency. [PUBLICATION ABSTRACT] We present a Lamarckian genetic algorithm (LGA) variant for flexible ligand-receptor docking which allows to handle a large number of degrees of freedom. Our hybrid method combines a multi-deme LGA with a recently published gradient-based method for local optimization of molecular complexes. We compared the performance of our new hybrid method to two non gradient-based search heuristics on the Astex diverse set for flexible ligand-receptor docking. Our results show that the novel approach is clearly superior to other LGAs employing a stochastic optimization method. The new algorithm features a shorter run time and gives substantially better results, especially with increasing complexity of the ligands. Thus, it may be used to dock ligands with many rotatable bonds with high efficiency. |
| Author | Rurainski, Alexander Fuhrmann, Jan Lenhof, Hans-Peter Neumann, Dirk |
| Author_xml | – sequence: 1 fullname: Fuhrmann, Jan – sequence: 2 fullname: Rurainski, Alexander – sequence: 3 fullname: Lenhof, Hans-Peter – sequence: 4 fullname: Neumann, Dirk |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/20082382$$D View this record in MEDLINE/PubMed |
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| Snippet | We present a Lamarckian genetic algorithm (LGA) variant for flexible ligand-receptor docking which allows to handle a large number of degrees of freedom. Our... We present a Lamarckian genetic algorithm (LGA) variant for flexible ligand‐receptor docking which allows to handle a large number of degrees of freedom. Our... |
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| SubjectTerms | Algorithms Comparative analysis Computer Simulation Genetic algorithms gradient based minimization Heuristic ligand-receptor docking Ligands meta-heuristic Models, Genetic Molecules Optimization Protein Conformation Proteins - chemistry Proteins - metabolism Stochastic models unconstrained minimization |
| Title | new Lamarckian genetic algorithm for flexible ligand-receptor docking |
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