Correlation Weights Of Local Invariants Research Articles

QSAR models of quail dietary toxicity based on the graph of atomic orbitals

Graphs of atomic orbitals (GAOs) have been used to represent molecular structures. We describe rules to convert the labelled hydrogen-filled graphs (LHFGs) into GAOs. The GAO is one possible way of taking account of the structure of atoms (i.e., atomic orbitals, such as 1s 1, 2p 2 and 3d 10) for QSPR/QSAR analyses. Optimization of correlation weights of local invariants (OCWLI) of the LHFGs and the GAOs was used to obtain a method of quail dietary toxicity modelling. Statistical characteristics of the models based on the OCWLI of GAO are better than those based on the OCWLI of the LHFGs.

QSAR of the testosterone binding globulin affinity by means of correlation weighting of local invariants of the graph of atomic orbitals

Numerical values of the testosterone binding globulin affinity have been modeled as a mathematical function of molecular structure in two versions of molecular structure elucidation: first, by hydrogen-filled molecular graphs (HFG); second, by the so-called graphs of atomic orbitals (GAO). Increased orders of Morgan extended connectivity in the HFG and GAO have been examined as local invariants. Using optimisation of the correlation weights of the above-mentioned invariants, quantitative structure–activity relationships (QSAR) have been obtained. Best statistical characteristics of these QSARs are derived in the case of the Morgan extended connectivity of first order in the GAO. They are as follows: n = 11, r 2 = 0.6540, s = 0.824, F = 17 (training set); n = 9, r 2 = 0.8791, s = 0.388, F = 51 (test set).

QSPR Modeling of Gibbs Free Energy of Organic Compounds by Weighting of Nearest Neighboring Codes

We examine the encoding of chemical structure of organic compounds by Labeled Hydrogen-Filled Graphs (LHFGs). Quantitative Structure-Property Relationships (QSPR) for a representative set of 150 organic molecules have been derived by means of the optimization of correlation weights of local invariants of the LHFGs. We have tested as local invariants Morgan extended connectivity of zero- and first order, numbers of path of length 2 (P2) and valence shells of distance of 2 (S2) associated with each atom in the molecular structure, and the Nearest Neighboring Codes (NNC). The best statistical characteristics for the Gibbs free energy has been obtained for the NNC weighting. Statistical parameters corresponding to this model are the following n = 100, r2 = 0.9974, s = 5.136 kJ/mol, F = 38319 (training set); n = 50, r2 = 0.9990, s = 3.405 kJ/mol, F = 48717 (test set). Some possible further developments are pointed out.

QSAR modelling of aldehyde toxicity against a protozoan, Tetrahymena pyriformis by optimization of correlation weights of nearest neighboring codes

By means of the so-called optimization of correlation weights of local invariants (OCWLI) in labeled hydrogen-filled graphs (LHFGs) models of toxicity for 33 aldehydes were constructed. The toxicity endpoint was Tetrahymena pyriformis, a protozoan. The training set included 16 chemicals and the test set 17. As local invariants of the LHFGs we considered extended connectivity values of various orders and nearest neighboring codes (NNC). The best model of toxicity was obtained using the NNC.

Prediction of alkane enthalpies by means of correlation weighting of Morgan extended connectivity in molecular graphs

Labeled hydrogen-filled graphs (LHFGs) together with graphs of atomic orbitals (GAOs) have been used to represent the molecular structure of alkanes. The GAO is an attempt at taking into account the structures of atoms (i.e., atomic orbitals such as, 1s 1, 2p 2, 3d 10) for QSPR/QSAR analyses. As a method of alkane enthalpies modeling, optimization of correlation weights of local invariants (OCWLI) of the LHFGs and the GAOs has been used. Statistical characteristics of such models based on the OCWLI of the GAO are better than those based on the OCWLI of the LHFGs.

QSAR modeling of dihydrofolate reductase inhibitory activity by correlation weighting of nearest neighboring codes

The nearest neighboring code (NNC) is a local graph invariant. The NNC of a given vertex of the labeled hydrogen filled graph (LHFG) is a function of atom composition of the vertex neighbors. By optimization correlation weights of local invariants of the LHFG one-variable models of dihydrofolate reductase inhibitory activity have been obtained. As local graph invariants Morgan extended connectivity of zero, first, and second orders as well as the NNCs have been used. Best such model has been obtained with the NNC. Statistical characteristics of the model are the follows: Training set: n=33, r 2=0.9273, s=0.155, F=395; Test set: n=35, r 2=0.8782, s=0.259, F=238; All structures: n=68, r 2=0.8855, s=0.213, F=511.

Improved Molecular Descriptors Based on the Optimization of Correlation Weights of Local Graph Invariants

We report the calculation of boiling points for several alkyl alcohols through the use of improved molecular descriptors based on the optimization of correlation weights of local invariants of graphs. As local invariants we have used the presence of different chemical elements (i.e. C, H, and O) and the existence of different vertex degree values (i.e. 1, 2, 3 and 4). The inherent flexibility of the chosen molecular descriptor seems to be rather suitable to obtain satisfactory enough predictions of the property under study. Comparison with other similar approximation reveals a very good behavior of the present method. The use of higher order polynomials do not seem to be necessary to improve results regarding the simple linear fitting equations. Some possible future extensions are pointed out in order to achieve a more definitive conclusion about this approximation.

Improved molecular descriptors to calculate boiling points based on the optimization of correlation weights of local graph invariants

We report the calculation of boiling points for several alkyl alcohols through the use of improved molecular descriptors based on the optimization of correlation weights of local invariants of graphs. As local invariants we have used the presence of different chemical elements (i.e. C, H, and O) and the existence of different vertex degree values (i.e. 1, 2, 3 and 4). The inherent flexibility of the chosen molecular descriptor seems to be rather suitable to obtain satisfactory predictions of the property under study. Comparison with other similar approximation reveals a very good behavior of the present method. The use of higher order polynomials do not seem to be necessary to improve the results regarding the simple linear fitting equations. Some possible future extensions are pointed out in order to achieve a more definitive conclusion about this approximation.

Prediction of heteroaromatic amine mutagenicity by means of correlation weighting of atomic orbital graphs of local invariants

Graphs of atomic orbitals (GAOs) have been used to represent molecular structures. Rules by which the labeled hydrogen-filled graphs (LHFGs) were converted into the GAOs are described. The GAO is an attempt at taking into account the structures of atoms (i.e. atomic orbitals, such as 1s 1, 2p 2, 3d 10) for QSPR/QSAR analyses. As a method of mutagenicity modeling, optimization of correlation weights of local invariants (OCWLI) of the LHFGs and the GAOs has been used. Statistical characteristics of such models based on the OCWLI of GAO are better than those based on the OCWLI of the LHFGs.

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.