Variable Connectivity Index as a Tool for Modeling Structure-Property Relationships

Randić, Milan; Pompe, Matevž; Mills, Denise; Basak, Subhash C.

doi:10.3390/91201177

Cited by 18 publications

(18 citation statements)

References 41 publications

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“…Graphs are widely used in cheminformatics to represent chemicals16: each vertex is related to an atom and each edge represents a chemical bond connecting two vertices. From those reduced representations of compounds, many authors suggested to compute various parameters (termed ‘graph indices’)10,17 expressing different molecular characteristics related to connectivity, the complexity of internal branching, etc., in order to discriminate and compare two chemical structures quantitatively. Hundreds of such indices10 have been proposed over the years, most of them being computed from the adjacency matrix and/or topological distance matrix.…”

Section: Methodsmentioning

confidence: 99%

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Using Graph Indices for the Analysis and Comparison of Chemical Datasets

Fourches¹,

Tropsha²

2013

Molecular Informatics

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In cheminformatics, compounds are represented as points in multidimensional space of chemical descriptors. When all pairs of points found within certain distance threshold in the original high dimensional chemistry space are connected by distance-labeled edges, the resulting data structure can be defined as Dataset Graph (DG). We show that, similarly to the conventional description of organic molecules, many graph indices can be computed for DGs as well. We demonstrate that chemical datasets can be effectively characterized and compared by computing simple graph indices such as the average vertex degree or Randic connectivity index. This approach is used to characterize and quantify the similarity between different datasets or subsets of the same dataset (e.g., training, test, and external validation sets used in QSAR modeling). The freely available ADDAGRA program has been implemented to build and visualize DGs. The approach proposed and discussed in this report could be further explored and utilized for different cheminformatics applications such as dataset diversification by acquiring external compounds, dataset processing prior to QSAR modeling, or (dis)similarity modeling of multiple datasets studied in chemical genomics applications.

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Section: Methodsmentioning

confidence: 99%

“…(iii) Randic connectivity index,17,18 χ R , is one of the most used topological descriptor to express the level of molecular branching, using vertex degrees of connected vertices …”

Section: Methodsmentioning

confidence: 99%

Using Graph Indices for the Analysis and Comparison of Chemical Datasets

Fourches¹,

Tropsha²

2013

Molecular Informatics

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“…The simple and valence connectivity indices defined and developed by Randic [34,35,[37][38][39], Kier [40][41][42] and Hall [43,44] are used in this work which can be expressed by the following equation. where m is the order of the connectivity index; k denotes a contiguous path type of fragment, which is divided into paths (P), clusters (C), etc; p denotes which type connectivity index is(simple, valence or other type); n m is the number of the relevant paths; δ i p is the connectivity index.…”

Section: Methodsmentioning

confidence: 99%

“…The molecular connectivity indices which were proposed 30 years ago, have been successfully used in the correlation of various physiochemical properties of organic substances [33][34][35] especially in the recent applications to computational molecular design studies [36]. In the previous works, correlations of aqueous solubility using molecular connectivity indices and other descriptors have been studied and demonstrated the possibility of molecular connectivity indices in modeling aqueous solubility.…”

Section: Introductionmentioning

confidence: 99%

Predicting Aqueous Solubility of Chlorinated Hydrocarbons by the MCI Approach

Wang

et al. 2006

IJMS

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Correlation for estimation of the aqueous solubility (logSw) of chlorinated hydrocarbons molecules is proposed. The MCI based quantitative structure-property relationship (QSPR) model proposed is predictive and requires only three connectivity indices in the calculation. The correlation equation obtained which is based on a training set of 50 chlorinated hydrocarbons has a correlation coefficient of 0.9670 and a standard error of 0.44 log 10 units. Application of the developed model to a testing set of 73 chlorinated hydrocarbons demonstrates that the new model is reliable with good predictive accuracy and simple formulation. Besides, the model does not require any experimental physicochemical properties in the calculation, so it is easy to apply, especially in cases where it is inconvenient or impossible to measure the physicochemical properties.

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“…The constructed augmented matrices are analogous to similar matrices that differentiate heteroatoms in molecules in the construction of the variable connectivity indices. [90][91][92][93][94][95][96][97][98][99][100][101][102][103] A similar approach of differentiation among proteomics maps associated with different drugs and other xenobiotic agents was used earlier in the literature on the mathematical characterization of proteomics maps using zigzag lines. [10,104] However, the normalizations used there were not adjusted to incorporate the dependence of matrix elements on the matrix size.…”

Section: Review Wwwq-chemorgmentioning

confidence: 99%

Milestones in graphical bioinformatics

Randić

Novič

Plavšić

2013

Int J of Quantum Chemistry

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After reviewing the field of graphical bioinformatics, we have selected two dozen of the most significant publications that represent milestones of graphical bioinformatics. These publications can be viewed as forming the backbone of graphical bioinformatics, the branch of bioinformatics that initiates analysis of DNA, RNA, and proteins by considering various graphical representations of these sequences. Graphical bioinformatics, a division of bioinformatics that analyzes sequences of DNA, RNA, proteins, and proteomics maps by developing and using tools of discrete mathematics and graph theory in particular, has expanded since the year 2000, although pioneering contributions date back to Hamory (1983) and Jeffrey (1990). We chronologically follow the development of graphical bioinformatics, without assuming that readers are familiar with discrete mathematics or graph theory. Readers unfamiliar with graph theory may even have some advantage over those who have been only superficially exposed to graph theory, inview of wide misconceptions and misinformation about chemical graph theory among quantum chemists, physical chemists, and medicinal chemists in past decades. © 2013 Wiley Periodicals, Inc.

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Variable Connectivity Index as a Tool for Modeling Structure-Property Relationships

Cited by 18 publications

References 41 publications

Using Graph Indices for the Analysis and Comparison of Chemical Datasets

Using Graph Indices for the Analysis and Comparison of Chemical Datasets

Predicting Aqueous Solubility of Chlorinated Hydrocarbons by the MCI Approach

Milestones in graphical bioinformatics

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