QSPR modeling of the enthalpy of formation from elements by means of correlation weighting of local invariants of atomic orbital molecular graphs

A quantitative structure–property modeling of the enthalpy of formation from elements by means of correlation weighting of local invariants of atomic orbital molecular graphs (AOMGs) is presented. The method is applied to a set comprising 51 hydrocarbon molecules and results are quite good, with lower average deviations than experimental uncertainties. Some possible applications and further extensions of the calculation procedure are pointed out.     

QSPR modeling: graph connectivity indices versus line graph connectivity indices

Five QSPR models of alkanes were reinvestigated. Properties considered were molecular surface-dependent properties (boiling points and gas chromatographic retention indices) and molecular volume-dependent properties (molar volumes and molar refractions). The vertex- and edge-connectivity indices were used as structural parameters. In each studied case we computed connectivity indices of alkane trees and alkane line graphs and searched for the optimum exponent. Models based on indices with an optimum exponent and on the standard value of the exponent were compared. Thus, for each property we generated six QSPR models (four for alkane trees and two for the corresponding line graphs). In all studied cases QSPR models based on connectivity indices with optimum exponents have better statistical characteristics than the models based on connectivity indices with the standard value of the exponent. The comparison between models based on vertex- and edge-connectivity indices gave in two cases (molar volumes and molar refractions) better models based on edge-connectivity indices and in three cases (boiling points for octanes and nonanes and gas chromatographic retention indices) better models based on vertex-connectivity indices. Thus, it appears that the edge-connectivity index is more appropriate to be used in the structure-molecular volume properties modeling and the vertex-connectivity index in the structure-molecular surface properties modeling. The use of line graphs did not improve the predictive power of the connectivity indices. Only in one case (boiling points of nonanes) a better model was obtained with the use of line graphs.     

A new approach for devising local graph invariants: Derived topological indices with low degeneracy and good correlation ability

A new approach is presented for obtaining graph invariants which have very high discriminating ability for different vertices within a graph. These invariants are obtained as the solution set (local invariant set, LOIS) of a system of linear equationsQ · X = R, whereQ is a topological matrix derived from the adjacency matrix of the graph, andR is a column vector which encodes either a topological property (vertex degree, number of vertices in the graph, distance sum) or a chemical property (atomic number). Twenty examples of LOOIs are given and their degeneracy and ordering ability of vertices is discussed. Interestingly, in some cases the ordering of vertices obtained by means of these invariants parallels closely the ordering from an entirely different procedure based on Hierarchically Ordered Extended Connectivities which was recently reported. New topological indices are easily constructed from LOISs. Excellent correlations are obtained for the boiling points and vaporization enthalpies of alkanesversus the topological index representing the sum of local vertex invariants. Les spectacular correlations with NMR chemical shifts, liquid phase density, partial molal volumes, motor octane numbers of alkanes or cavity surface areas of alcohols emphasize, however, the potential of this approach, which remains to be developed in the near future.     

Degeneracy of some matrix graph invariants

New matrices associated with graphs and induced global and local topological indices of molecular graphs were proposed recently by Diudea, Minailiuc and Balaban. These matrices in canonical form are matrix graph invariants. A combined degeneracy of such invariants is considered. For every case of degeneracy corresponding graphs are presented.     

QSAR modeling of toxicities of ionic liquids toward Staphylococcus aureus using SMILES and graph invariants

Structural Chemistry - Ionic liquids (ILs) have been popular in many industrial and chemical processes, like antimicrobial properties, solvents, and synthesis of new compounds with antioxidant...     

Automated QSPR modeling and data curation of physicochemical properties using KNIME platform: Prediction of partition coefficients

In the last two decades, the volumes of chemical and biological data are constantly increasing. The problem of converting data sets into knowledge is both expensive and time-consuming, as a result a workflow technology with platforms such as KNIME, was built up to facilitate searching through multiple heterogeneous data sources and filtering for specific criteria then extracting hidden information from these large data. Before any QSAR modeling, a manual data curation is extremely recommended. However, this can be done, for small datasets, but for the extensive data accumulated recently in public databases a manual process of big data will be hardly feasible. In this work, we suggest using KNIME as an automated solution for workflow in data curation, development, and validation of predictive QSAR models from a huge dataset.In this study, we used 250250 structures from NCI database, only 3520 compounds could successfully pass through our workflow safely with their corresponding experimental log P, this property was investigated as a case study, to improve some existing log P calculation algorithms.     

Predicting properties of molecules using graph invariants

Topological indices (TIs) have been used to study structure-activity relationships (SAR) with respect to the physical, chemical, and biological properties of congeneric sets of molecules. Since there are many TIs and many are correlated, it is important that we identify redundancies and extract useful information from TIs into a smaller number of parameters. Moreover, it is important to determine if TIs, or parameters derived from TIs, can be used for global SAR models of diverse sets of chemicals. We calculated seventy-one TIs for three groups of molecules of increasing complexity and diversity: (a) 74 alkanes, (b) 29 alkylbenzenes, and (c) 37 polycyclic aromatic hydrocarbons (PAHs). Principal components analysis (PCA) revealed that a few principal components (PCs) could extract most of the information encoded by the seventy-one TIs. The structural basis of the first few PCs could be derived from their pattern of correlation with individual TIs. For the three sets of molecules, viz. alkanes, alkylbenzenes and PAHs, PCs were able to predict the boiling points reasonably well. Also, for the combined set of 140 chemicals consisting of the alkanes, alkylbenzenes and PAHs, the derived PCs were not as effective in predicting properties as in the case of individual classes of compounds.