Developmment and Application of Ligand-based and Structure-based Computational Drug Discovery Tools Based on Frequent Subgraph Mining of Chemical Structures

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Last Modified
  • March 20, 2019
Creator
  • Khashan, Raed Saeed
    • Affiliation: Eshelman School of Pharmacy, Division of Chemical Biology and Medicinal Chemistry
Abstract
  • 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.
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  • In Copyright
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  • Tropsha, Alexander
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  • Open access
Date uploaded
  • October 19, 2010

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