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Topological Indices and Related Descriptors in QSAR and QSPR
1. No-Free-Lunch Molecular Descriptor in QSAR and QSPAR 2. The Graph Description of Chemical Structures 3. Matrices and Structural Descriptors Computed from Molecular Graph Distances 4. Molecular
Highly discriminating distance-based topological index
Abstract A new topological index J (based on distance sums s i as graph invariants) is proposed. For unsaturated or aromatic compounds, fractional bond orders are used in calculating s i . The
Topological indices based on topological distances in molecular graphs
Three new distance—based topological indices are described; two of them, D and D1 (mean distance topological indices, for any graphs, and for acyclic graphs, respectively) have a modest
Applications of graph theory in chemistry
  • A. Balaban
  • Mathematics, Computer Science
    J. Chem. Inf. Comput. Sci.
  • 1 August 1985
TLDR
The complete set of all poasible monocyclic aromatic and heteroaromatic compounds may be explored by a mmbination of Pauli's principle, P6lya's theorem.
Topological indices for structure-activity correlations
This chapter deals with the description of the main topological indices and of related indicatros for molecular constitution used in structure-activity relationships (QSAR). The topological indices
From chemical topology to three-dimensional geometry
From Chemical Graphs to 3D Molecular Modeling A.T. Balaban. Descriptors of Molecular Shape in 3D P.G. Mezey. Algorithms for 3D Molecular Design and Applications to QSAR O. Mekenyan, G. Veith. Use of
Chemical graphs—V : Enumeration and proposed nomenclature of benzenoid cata-condensed polycyclic aromatic hydrocarbons
Abstract A modified definition is proposed for cata-condensed aromatic hydrocarbons. A formula is devised for the number of isomers of non-branched cata-condensed arenes in function of the number of
Prediction of Mutagenicity of Aromatic and Heteroaromatic Amines from Structure: A Hierarchical QSAR Approach
TLDR
A hierarchical approach is used to develop class-specific quantitative structure-activity relationship (QSAR) models for the prediction of mutagenicity of a set of 95 aromatic and heteroaromatic amines, finding that the inclusion of log P, geometric, and quantum chemical parameters does not result in significantly improved predictive models.
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