Highly discriminating distance-based topological index

By A. Balaban. 1982

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Citations in the corpus

Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction (2017, background): “On other hand, there are descriptors such as the Balaban’s J index [2] that may not be as intuitive, but nevertheless is an abstract topological description of a chemical that has been useful in various modeling studies.”
DMAX - Matrix of Dominant Distances in a Graph * (2013, methodology): “However, from DMAX one can also construct an invariant, the construction of which is related to Balaban’s J index [24], which is based on using row sums of the distance matrix to construct a topological index analogous to the connectivity index [31].”
Uniquely Discriminating Molecular Structures Using Novel Eigenvalue-Based Descriptors (2012, background): “1 Introduction Structural descriptors for investigating molecular structures have been extensively explored in mathematical, structural and computational chemistry, and related disciplines; see [3–5, 7, 55].”
ChemNet: A Transferable and Generalizable Deep Neural Network for Small-Molecule Property Prediction (2017, background): “On other hand, there are descriptors such as the Balaban’s J index [21] that may not be as intuitive, but nevertheless is an abstract topological description of a molecule that has been useful in QSAR/QSPR modeling studies.”
The Use of Topological Indices in QSAR and QSPR Modeling (2017, methodology): “The Balaban index (Balaban 1982), also called the average distance sum connectivity index, is computed as follows: the numbers of edges from atom i to all other atoms in a molecule are summed, and this procedure is repeated for all other atoms.”