Connectivity polynomial and long-range contributions in the molecular connectivity model

Probing the opportunities for designing anthelmintic leads by sub-structural topology-based QSAR modelling

A quantitative structure-activity (QSAR) model has been developed for enriched tubulin inhibitors, which were retrieved from sequence similarity searches and applicability domain analysis. Using partial least square (PLS) method and leave-one-out (LOO) validation approach, the model was generated with the correlation statistics of [Formula: see text] and [Formula: see text] of 0.68 and 0.69, respectively. The present study indicates that topological descriptors, viz. BIC, CH_3_C, IC, JX and Kappa_2 correlate well with biological activity. ADME and toxicity (or ADME/T) assessment showed that out of 260 molecules, 255 molecules successfully passed the ADME/T assessment test, wherein the drug-likeness attributes were exhibited. These results showed that topological indices and the colchicine binding domain directly influence the aetiology of helminthic infections. Further, we anticipate that our model can be applied for guiding and designing potential anthelmintic inhibitors.

On molecular topological properties of diamond-like networks

The Randić (product) connectivity index and its derivative called the sum-connectivity index are well-known topological indices and both of these descriptors correlate well among themselves and with the π-electronic energies of benzenoid hydrocarbons. The general n connectivity of a molecular graph G is defined as [Formula: see text] and the n sum connectivity of a molecular graph G is defined as [Formula: see text], where the paths of length n in G are denoted by [Formula: see text] and the degree of each vertex vi is denoted by di. In this paper, we discuss third connectivity and third sum-connectivity indices of diamond-like networks and compute analytical closed results of these indices for diamond-like networks.

Design of novel antituberculosis compounds using graph-theoretical and substructural approaches

The increasing resistance of Mycobacterium tuberculosis to the existing drugs has alarmed the worldwide scientific community. In an attempt to overcome this problem, two models for the design and prediction of new antituberculosis agents were obtained. The first used a mixed approach, containing descriptors based on fragments and the topological substructural molecular design approach (TOPS-MODE) descriptors. The other model used a combination of two-dimensional (2D) and three-dimensional (3D) descriptors. A data set of 167 compounds with great structural variability, 72 of them antituberculosis agents and 95 compounds belonging to other pharmaceutical categories, was analyzed. The first model showed sensitivity, specificity, and accuracy values above 80% and the second one showed values higher than 75% for these statistical indices. Subsequently, 12 structures of imidazoles not included in this study were designed, taking into account the two models. In both cases accuracy was 100%, showing that the methodology in silico developed by us is promising for the rational design of antituberculosis drugs.

Ordering of alkane isomers by means of connectivity indices

The connectivity index of an organic molecule whose molecular graph is Gis defined as C(?)=?(?u?v)??where ?u is the degree of the vertex u in G, where the summation goes over all pairs of adjacent vertices of G and where ? is a pertinently chosen exponent. The usual value of ? is ?1/2, in which case ?=C(?1/2) is referred to as the Randic index. The ordering of isomeric alkanes according to ??follows the extent of branching of the carbon-atom skeleton. We now study the ordering of the constitutional isomers of alkanes with 6 through 10 carbon atoms with respect to C(?) for various values of the parameter ?. This ordering significantly depends on ?. The difference between the orderings with respect to ??and with respect to C(?) is measured by a function ??and the ?-dependence of ??was established.

Combination of 2D-, 3D-connectivity and quantum chemical descriptors in QSPR. Complexation of alpha- and beta-cyclodextrin with benzene derivatives

Quantitative models are found to describe the complexation of alpha- and beta-cyclodextrin with mono- and 1,4-disubstituted benzene derivatives by using combinations of 2D-, 3D-connectivity and quantum chemical molecular descriptors. The association constants (K(a)) for the inclusion complexation of cyclodextrins and benzene derivatives are calculated by the models found with a high degree of precision. These models also permit the interpretation of the driving forces of such complexation processes. In the case of the complexation of alpha-cyclodextrin with benzene derivatives these driving forces are mainly the electronic repulsion between frontier orbitals of the host and guest molecules. However, the complexation of beta-cyclodextrin with benzene derivatives is controlled by topological and topographic parameters indicating the relevance of the van der Waals and hydrophobic interactions. We also carried out molecular modeling studies showing that for alpha-cyclodextrin complexes the benzene ring is outside the cavity of the cyclodextrin, while in beta-cyclodextrin they penetrate deeply into the apolar and hydrophobic cavity of the host, which explain the differences in the driving forces for both complexation processes.

3D connectivity indices in QSPR/QSAR studies

Topographic (3D) molecular connectivity indices based on molecular graphs weighted with quantum chemical parameters are used in QSPR and QSAR studies. These descriptors were compared to 2D connectivity indices (vertex and edge ones) and to quantum chemical descriptors in modeling partition coefficient (log P) and antibacterial activity of 2-furylethylene derivatives. In describing log P the 3D connectivity indices produced a significant improvement (more than 29%) in the predictive capacity of the model compared to those derived with topological and quantum chemical descriptors. The best linear discriminant model for classifying antibacterial activity of these compounds was also obtained with the use of 3D connectivity indices. The global percent of good classification obtained with 3D and 2D connectivity as well as quantum chemical descriptors were 94.1, 91.2, and 88.2, respectively. In general, all these models predict correctly the antibacterial activity of a set of nine new 2-furylethylene derivatives. The best result is obtained with 3D connectivity indices that classified correctly 100% of these compounds versus 88.9% obtained with 2D connectivity or quantum chemical descriptors.

On structural interpretation of several distance related topological indices

We consider the role that individual bonds play in bond-additivities in order to better understand the structural basis of various topological indices. In particular we consider indices closely related to the Wiener index (W) and the distance matrix and search for optimal weights of terminal and interior CC bonds in alkanes for a selection of physicochemical properties. It is interesting to note that different properties are associated with different relative roles of the exterior and the interior CC bonds.