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Construction and Systematical Symmetric Studies of a Series of Supramolecular Clusters with Binary or Ternary Ammonium Triphenylacetates
Published on: February 15, 2016
"Social" network of isomers based on bond count distance: algorithms
Tina M Kouri1, Mahendra Awale, James K Slyby
1Department of Computer Science and Engineering, University of South Florida , 4202 E. Fowler Avenue, Tampa, Florida 33620, United States.
This study presents isomer networks, a novel method for analyzing chemical isomer space using reaction step counts. Algorithms were developed to address computational costs, enabling efficient network analysis for diverse molecular structures.
Area of Science:
- Computational Chemistry
- Cheminformatics
- Network Science
Background:
- Analyzing the vast space of chemical isomers is crucial for understanding molecular properties and reactions.
- Current methods for isomer analysis can be computationally intensive, limiting their application.
Purpose of the Study:
- To introduce and explore the concept of isomer networks as a new framework for isomer space analysis.
- To develop efficient algorithms for computing and analyzing these networks.
- To correlate isomer network properties with established molecular descriptors.
Main Methods:
- Development of algorithms for constructing isomer networks based on reaction step counts.
- Generation of diverse isomer subsets for nicotine, tyrosine, and phenmetrazine using molecular quantum number nearest neighbors.
- Analysis of computed isomer networks and their correlation with extended connectivity fingerprints.
Main Results:
- Demonstrated the feasibility of constructing isomer networks for complex molecules.
- Identified correlations between isomer network topology and extended connectivity fingerprints.
- Provided a new perspective on isomer space exploration.
Conclusions:
- Isomer networks offer a valuable alternative for visualizing and analyzing isomer relationships.
- The developed algorithms improve the computational efficiency of isomer network analysis.
- This approach enhances the understanding of molecular diversity and structure-activity relationships.
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