On the centrality of vertices of molecular graphs.

For acyclic systems the center of a graph has been known to be either a single vertex of two adjacent vertices, that is, an edge. It has not been quite clear how to extend the concept of graph center to polycyclic systems. Several approaches to the graph center of molecular graphs of polycyclic graphs have been proposed in the literature. In most cases alternative approaches, however, while being apparently equally plausible, gave the same results for many molecules, but occasionally they differ in their characterization of molecular center. In order to reduce the number of vertices that would qualify as forming the center of the graph, a hierarchy of rules have been considered in the search for graph centers. We reconsidered the problem of "the center of a graph" by using a novel concept of graph theory, the vertex "weights," defined by counting the number of pairs of vertices at the same distance from the vertex considered. This approach gives often the same results for graph centers of acyclic graphs as the standard definition of graph center based on vertex eccentricities. However, in some cases when two nonequivalent vertices have been found as graph center, the novel approach can discriminate between the two. The same approach applies to cyclic graphs without additional rules to locate the vertex or vertices forming the center of polycyclic graphs, vertices referred to as central vertices of a graph. In addition, the novel vertex "weights," in the case of acyclic, cyclic, and polycyclic graphs can be interpreted as vertex centralities, a measure for how close or distant vertices are from the center or central vertices of the graph. Besides illustrating the centralities of a number of smaller polycyclic graphs, we also report on several acyclic graphs showing the same centrality values of their vertices.

The sum-connectivity index--an additive variant of the Randic connectivity index.

This review discusses structure-property modeling applications of a novel variant of the Randic connectivity index that is called the sum-connectivity index. We compare published one-descriptor quantitative structure-property relationship (QSPR) models obtained with the new sum-connectivity index and with the Randic connectivity index, called here the product-connectivity index. Additionally, the efficiency of both variants of connectivity indices in QSPR modeling is tested on five datasets of alkanes and two datasets of polycyclic hydrocarbons. Several physicochemical properties of alkanes (i.e. boiling and melting points, retention index, molar volume, molar refraction, heat of vaporization, standard Gibbs energy of formation, critical temperature, critical pressure, surface tension, density) and π- electronic energies of two sets of polycyclic hydrocarbons were correlated with the product- and sum-connectivity indices. A comparison of these QSPR models shows that both variants of connectivity indices are equivalent, and only slightly (but not significantly) better results are obtained with the sum-connectivity index. Inter-correlations between the product- and sum-connectivity indices are mostly linear with a slope very close to 1.0 for alkanes, and with a slope more different from 1.0 (0.88) for polycyclic compounds. The comparative analysis presented here supports the use of the sumconnectivity index in QSPR/QSAR studies together with the product-connectivity index. Further studies on larger and more heterogeneous datasets should test the sum-connectivity index in QSPR/QSAR models.

Common vertex matrix: a novel characterization of molecular graphs by counting.

We present a novel matrix representation of graphs based on the count of equal-distance common vertices to each pair of vertices in a graph. The element (i, j) of this matrix is defined as the number of vertices at the same distance from vertices (i, j). As illustrated on smaller alkanes, these novel matrices are very sensitive to molecular branching and the distribution of vertices in a graph. In particular, we show that ordered row sums of these novel matrices can facilitate solving graph isomorphism for acyclic graphs. This has been illustrated on all undecane isomers C11H24 having the same path counts (total of 25 molecules), on pair of graphs on 18 vertices having the same distance degree sequences (Slater's graphs), as well as two graphs on 21 vertices having identical several topological indices derived from information on distances between vertices.

Conjugated circuits currents in hexabenzocoronene and its derivatives formed by joining proximal carbons.

We report on calculated CC bond currents for a dozen derivatives of hexabenzocoroenene in which one or more proximal carbon atoms at the molecular periphery have been bridged. The approach that we use is graph-theoretical in nature, following our outline of this method in 2003, which is based on finding all conjugated circuits in all Kekulé valence structures of these molecules. To the π-electrons having 4n + 2 π-electrons are assigned anticlockwise π-electron currents and to conjugated circuits having 4n π-electrons are assigned π-electron currents. One may summarize the results reported in this work by stating that CC bond currents in the compounds considered decrease on going from peripheral rings to the central ring of the molecule, and also that CC bond currents decrease by insertion of bridges to proximal peripheral benzenoid rings.

Applying the conjugated circuits method to Clar structures of [n]phenylenes for determining resonance energies.

We consider the aromaticity of biphenylene and structurally related linear or angular [n]phenylenes for which the direct application of the model of conjugated circuits does not offer valid expressions for resonance energy and aromaticity. We located the cause of this problem as being due to Kekulé valence structures in which neighboring benzenoid rings are connected by two CC double bonds. By restricting the selection of Kekulé valence structures to those that contribute to Clar structures of such systems, we were able to show that linear and angular [n]phenylenes have approximately similar resonance energies, with angular [n]phenylenes being slightly more stable due to second order contributions arising from disjoint conjugated circuits. Expressions for resonance energies of [n]phenylenes up to n = 8 are listed and recursion expressions for higher n values are outlined.

Algebraic clar formulas - numerical representation of clar structural formula.

We outline the construction of an algebraic (numerical) representation for Clar's valence formulas which in their geometrical form are illustrated with π-aromatic sextets as inscribed circles in benzenoid rings.

Study of proteome maps using partial ordering.

This paper describes numerical characterization of proteome maps based on partial ordering of protein spots with respect to the mass and the charge. The partial ordering diagram is embedded directly over the 2D map and the corresponding adjacency matrix is constructed. The adjacency matrix is augmented by including the information on the abundance of proteins in a gel as suitably scaled diagonal entries of the matrix. The approach is illustrated on proteome maps of Anderson et al. (1996) based on experimental results from liver cells of rats exposed to four peroxisome proliferators. We used the leading eigenvectors of the adjacency matrices as maps descriptors in order to determine the degree of similarity between proteome maps.

Novel graph distance matrix.

We have introduced novel distance matrix for graphs, which is based on interpretation of columns of the adjacency matrix of a graph as a set of points in n-dimensional space, n being the number of vertices in the graph. Numerical values for the distances are based on the Euclidean distance between n points in n-dimensional space. In this way, we have combined the traditional representation of graphs (drawn as 2D object of no fixed geometry) with their representation in n-dimensional space, defined by a set of n-points that lead to a representation of definite geometry. The novel distance matrix, referred to as natural distance matrix, shows some structural properties and offers novel graph invariants as molecular descriptors for structure-property-activity studies. One of the novel graph descriptors is the modified connectivity index in which the bond contribution for (m, n) bond-type is given by 1/ radical(m + n), where m and n are the valence of the end vertices of the bond. The novel distance matrix (ND) can be reduced to sparse distance-adjacency matrix (DA), which can be viewed as specially weighted adjacency matrix of a graph. The quotient of the leading eigenvalues of novel distance-adjacency matrix and novel distance matrix, as illustrated on a collection of graphs of chemical interest, show parallelism with a simple measure of graph density, based on the quotient of the number of edges in a graph and the maximal possible number of edges for graphs of the same size.

Graphical representation of proteins as four-color maps and their numerical characterization.

We put forward a novel compact 2-D graphical representation of proteins based on the concept of virtual genetic code and a four-color map. The novel graphical representation uniquely represents proteins and allows one to easily and quickly visually observe and inspect similarity/dissimilarity between them. It also leads to a novel protein descriptor, a 10-dimensional vector derived from a novel structure matrix S associated with the map. The introduced numerical characterization of proteins is not only useful for their comparative study, but also for cataloguing information on a single protein. The approach is illustrated with the A chain of human insulin and the A chain of human insulin analogue glargine.

Canonical labeling of proteome maps.

We propose a canonical labeling of proteome maps, which enables one to sort and catalog the maps in a simple way. The canonical label of a proteome map is based on the canonical labeling of vertexes of Hasse diagram embedded in the map resulting in the adjacency matrix, the rows of which when viewed as binary numbers are the smallest possible such numbers. The use of the approach in documentation is illustrated with the proteome maps of liver cells of healthy male Fisher F344 rats and the rats treated with different peroxisome proliferators.

Modeling the octanol-water partition coefficients by an optimized molecular connectivity index.

A procedure that makes it possible to generate a coherent model for prediction of the octanol-water partition coefficient within the molecular connectivity formalism was put forward. The method is based on the optimization of weights for corresponding skeletal atoms and is similar to the method for calculation of a variable connectivity index proposed by Randic. In contrast to Randic's method, we incorporate in the algorithm the possibility that the contribution of a term describing a carbon-heteroatom bond may be negative. When tested on a set of about 300 structurally diverse organic molecules, our procedure proved to be superior to the standard valence connectivity method. External validation on a smaller set of compounds confirmed the superiority of our procedure with respect to the standard one. Intramolecular interactions, which are operative in more complex compounds, are treated in a similar fashion to that in the Hansch-Leo or Rekker methods, by inclusion of empirical correction factors.

Novel map descriptors for characterization of toxic effects in proteomics maps.

We consider a novel numerical characterization of proteomics maps based on the construction of a graph obtained by connecting all protein spots in a proteomics map that are at distance equal to, or smaller than, a critical distance D(c). We refer to the so constructed graph as a cluster graph and we calculate four associated characteristic matrices, previously considered in the literature: (1) the Euclidean-distance matrix ED; (2) the neighborhood-distance matrix ND; (3) the path-distance matrix based on the shortest paths between connected spots PD; and (4) the quotient matrix Q, the elements of which are given as the quotient of the corresponding elements of ED and ND matrices. Numerical descriptors for proteomics maps include in particular the leading eigenvalue of the Q matrix and the family of associated "higher order" matrices defined as powers of Q. These map descriptors show considerable sensitivity to perturbations of proteomics maps by toxicants.