Linked Life Data

18777161

Source:http://linkedlifedata.com/resource/pubmed/id/18777161

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2008-12-10
pubmed:abstractText
A new optimization model of molecular docking is proposed, and a fast flexible docking method based on an improved adaptive genetic algorithm is developed in this paper. The algorithm takes some advanced techniques, such as multi-population genetic strategy, entropy-based searching technique with self-adaptation and the quasi-exact penalty. A new iteration scheme in conjunction with above techniques is employed to speed up the optimization process and to ensure very rapid and steady convergence. The docking accuracy and efficiency of the method are evaluated by docking results from GOLD test data set, which contains 134 protein-ligand complexes. In over 66.2% of the complexes, the docked pose was within 2.0 A root-mean-square deviation (RMSD) of the X-ray structure. Docking time is approximately in proportion to the number of the rotatable bonds of ligands.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
1573-4951
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
23
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1-12
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
An improved adaptive genetic algorithm for protein-ligand docking.
pubmed:affiliation
Department of Computer Science and Engineering, School of Electronic and Information Engineering, Dalian University of Technology, Dalian, 116023, China.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't