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Development and application of ligan...

Khashan, Raed Saeed.

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Development and application of ligand-based and structure-based computational drug discovery tools based on frequent subgraph mining of chemical structures.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Development and application of ligand-based and structure-based computational drug discovery tools based on frequent subgraph mining of chemical structures./
Author:	Khashan, Raed Saeed.
Description:	148 p.
Notes:	Adviser: Alexander Tropsha.
Contained By:	Dissertation Abstracts International68-06B.
Subject:	Biology, Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3272685
ISBN:	9780549123736

Development and application of ligand-based and structure-based computational drug discovery tools based on frequent subgraph mining of chemical structures.
Khashan, Raed Saeed.

Development and application of ligand-based and structure-based computational drug discovery tools based on frequent subgraph mining of chemical structures. - 148 p.

Adviser: Alexander Tropsha.

Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2007.

Recent development in subgraph mining tools resulted in faster and more efficient algorithms that facilitate exploring the information encoded in data that can be represented by graphs. In this dissertation, we apply the graph mining technique to design ligand-based and structure-based computational drug discovery tools. For ligand-based drug design, molecules in a dataset will be represented by graphs, and subgraph mining tools will be used to find the frequent subgraphs (chemical fragments) that occur in at least a certain percentage of the ligands in the dataset. These chemical fragments will be used as molecular descriptors for the quantitative structure-activity relationship (QSAR) studies. They will also be used for identifying the pharmacophores responsible for the activity as well as the toxicophores responsible for the toxicity of a datasets of molecules. For the structure-based drug design, interacting atoms at the interface of a set of protein-ligand complexes will be represented by graphs. Frequent subgraphs identified will define the patterns of chemical interactions at the interface, which will be used to pose-score docked complexes to identify the correct docking pose.

ISBN: 9780549123736Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Development and application of ligand-based and structure-based computational drug discovery tools based on frequent subgraph mining of chemical structures.
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020 $a 9780549123736
035 $a (UMI)AAI3272685
035 $a AAI3272685
040 $a UMI $c UMI
100 1 $a Khashan, Raed Saeed. $3 1285237
245 1 0 $a Development and application of ligand-based and structure-based computational drug discovery tools based on frequent subgraph mining of chemical structures.
300 $a 148 p.
500 $a Adviser: Alexander Tropsha.
500 $a Source: Dissertation Abstracts International, Volume: 68-06, Section: B, page: 3715.
502 $a Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2007.
520 $a Recent development in subgraph mining tools resulted in faster and more efficient algorithms that facilitate exploring the information encoded in data that can be represented by graphs. In this dissertation, we apply the graph mining technique to design ligand-based and structure-based computational drug discovery tools. For ligand-based drug design, molecules in a dataset will be represented by graphs, and subgraph mining tools will be used to find the frequent subgraphs (chemical fragments) that occur in at least a certain percentage of the ligands in the dataset. These chemical fragments will be used as molecular descriptors for the quantitative structure-activity relationship (QSAR) studies. They will also be used for identifying the pharmacophores responsible for the activity as well as the toxicophores responsible for the toxicity of a datasets of molecules. For the structure-based drug design, interacting atoms at the interface of a set of protein-ligand complexes will be represented by graphs. Frequent subgraphs identified will define the patterns of chemical interactions at the interface, which will be used to pose-score docked complexes to identify the correct docking pose.
590 $a School code: 0153.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Health Sciences, Pharmacology. $3 1017717
690 $a 0419
690 $a 0715
710 2 $a The University of North Carolina at Chapel Hill. $b Pharmaceutical Sciences. $3 1285238
773 0 $t Dissertation Abstracts International $g 68-06B.
790 $a 0153
790 1 0 $a Golbraikh, Alexander $e committee member
790 1 0 $a Lee, Andrew $e committee member
790 1 0 $a Tropsha, Alexander, $e advisor
790 1 0 $a Wang, Wei $e committee member
790 1 0 $a Zheng, Weifan $e committee member
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3272685