Quantitative relationships between molecular structures, environmental temperatures and octanol-air partition coefficients of PCDD/Fs

A concise quantitative model that incorporates information on both environmental temperature M and molecular structures, for logarithm of octanol-air partition coefficient (K-OA) to base 10 (logK(OA)) of PCDDs, was developed. Partial least squares (PLS) analysis together with 14 quantum chemical descriptors were used to develop the quantitative relationships between structures, environmental temperatures and properties (QRSETP) model. It has been validated that the obtained QRSETP model can be used to predict logK(OA) of other PCDDs. Molecular size, environmental temperature (T), q(+) (the most positive net atomic charge on hydrogen or chlorine atoms in PCDD molecules) and E-LUMO (the energy of the lowest unoccupied molecular orbital) are main factors governing logK(OA) of PCDD/Fs under study. The intermolecular dispersive interactions and thus the size of the molecules play a leading role in governing logK(OA). The more chlorines in PCDD molecules, the greater the logK(OA) values. Increasing E-LUMO values of the molecules leads to decreasing logK(OA) values, implying possible intermolecular interactions between the molecules under study and octanol molecules. Greater q(+) values results in greater intermolecular electrostatic repulsive interactions between PCDD and octanol molecules and smaller logK(OA) values. (C) 2002 Elsevier Science B.V. All rights reserved.

Quantitative structure-property relationships for octanol-air partition coefficients of polychlorinated biphenyls

Based on nine quantum chemical descriptors computed by PM3 Hamiltonian, using partial least squares analysis, a significant quantitative structure-property relationship for the logarithm of octanol-air partition coefficients (log K-OA) of polychlorinated biphenyls (PCBs) was obtained. The cross-validated Q(cum)(2) value of the model is 0.962, indicating a good predictive ability. The intermolecular dispersive interactions and thus the size of the PCB molecules play a key role in governing log K-OA. The greater the size of PCB molecules, the greater the log K-OA values. Increasing E-LUMO (the energy of the lowest unoccupied molecular orbital) values of the PCBs leads to decreasing log K-OA values, indicating possible interactions between PCB and octanol molecules. Increasing Q(Cl)(+) (the most positive net atomic charges on a chlorine atom) and Q(C)(-) (the largest negative net atomic charge on a carbon atom) values of PCBs results in decreasing log K-OA values, implying possible intermolecular electrostatic interactions between octanol and PCB molecules. (C) 2002 Elsevier Science Ltd. All rights reserved.

Quantitative structure-property relationships (QSPRs) on direct photolysis of PCDDs

By the use of partial least squares (PLS) method and 27 quantum chemical descriptors computed by PM3 Hamiltonian, a statistically significant QSPR were obtained for direct photolysis quantum yields (Y) of selected Polychlorinated dibenzo-p-dioxins (PCDDs). The QSPR can be used for prediction. The direct photolysis quantum yields of the PCDDs are dependent on the number of chlorine atoms bonded with the parent structures, the character of the carbon-oxygen bonds, and molecular polarity. Increasing bulkness and polarity of PCDDs lead to decrease of log Y values. Increasing the frontier molecular orbital energies (E-lumo and E-homo) and heat of formation (HOF) values leads to increase of log Y values. (C) 2001 Elsevier Science Ltd. All rights reserved.

Quantitative structure-property relationship study on reductive dehalogenation of selected halogenated aliphatic hydrocarbons in sediment slurries

In this study, by the use of partial least squares (PLS) method and 26 quantum chemical descriptors computed by PM3 Hamiltonian, a quantitative structure-property relationship (QSPR) model was developed for reductive dehalogenation rate constants of 13 halogenated aliphatic compounds in sediment slurry under anaerobic conditions. The model can be used to explain the dehalogenation mechanism. Halogenated aliphatic compounds with great energy of the lowest unoccupied molecular orbital (E-lumo), total energy (TE), electronic energy (EE), the smallest bond order of the carbon-halogen bonds (BO) and the most positive net atomic charges on an atom of the molecule (q(+)) values tend to be reductively dehalogenated slow, whereas halogenated aliphatic compounds with high values of molecular weight (Mw), average molecular polarizability (a) and core-core repulsion energy (CCR) values tend to be reductively dehalogenated fastest. (C) 2001 Published by Elsevier Science Ltd.

Quantitative structure-property relationship studies on n-octanol/water partitioning coefficients of PCDD/Fs

Based on some fundamental quantum chemical descriptors computed by PM3 Hamiltonian, by the use of partial least-squares (PLS) analysis, a significant quantitative structure-property relationship (QSPR) model for logK(ow) of polychlorinated dibenzo-p-dioxins and dibenzo-p-furans (PCDD/Fs) was obtained. The QSPR can be used for prediction. The intermolecular dispersive interactions and thus the bulkness of the PCDD/Fs are the main factors affecting the logK(ow). The more chlorines in the PCDD/F molecule, the greater the logK(ow) values. (C) 2001 Elsevier Science Ltd. All rights reserved.

Quantitative relationship between chromatographic retentions and molecular structures of polychlorinated dibenzo-p-dioxins (PCDDs)

A new approach to study the quantitative relationships between chromatographic retentions and molecular structures of polychlorinated dibenzo-p-dioxins (PCDDs) is described. The retention equations of PCDDs log k' = A + B/T in gas chromatography (GC) are used to evaluate the properties of the regression coefficients A and B, which have been widely accepted as highly reliable chromatographic retentions. The quantitative relationships between the A, B values and the molecular structures are found. The molecular descriptors given for the first time in this article are very effective. As a result, the regression equations are derived with correlation coefficients greater than 0.9995. The A, B values of PCDDs with no standards available have been predicted according to these relationships. They are very useful in chromatographic identification. The retention times of all PCDDs can be conveniently predicted at any temperature program. Compared with the data obtained from the relevant experiments, the results of prediction are very accurate. (C) 2000 Elsevier Science Ltd. All rights reserved.

Correcting the systematic error of the density functional theory calculation: the alternate combination approach of genetic algorithm and neural network

The alternate combinational approach of genetic algorithm and neural network (AGANN) has been presented to correct the systematic error of the density functional theory (DFT) calculation. It treats the DFT as a black box and models the error through external statistical information. As a demonstration, the AGANN method has been applied in the correction of the lattice energies from the DFT calculation for 72 metal halides and hydrides. Through the AGANN correction, the mean absolute value of the relative errors of the calculated lattice energies to the experimental values decreases from 4.93% to 1.20% in the testing set. For comparison, the neural network approach reduces the mean value to 2.56%. And for the common combinational approach of genetic algorithm and neural network, the value drops to 2.15%. The multiple linear regression method almost has no correction effect here.

NMR and pattern recognition studies on the acute biochemical effects of Lu(NO3)(3)

Pattern recognition methods were applied to the analysis of 600 MHz H-1 NMR spectra of urine from rats dosed with compounds that induced organ-specific damage in the liver and kidney. Male Wistar rats were separated into groups (n=4) and each was treated with one of following compounds: HgCl2, CCl4, Lu(NO3)(3) and Changle (a kind of rare earth complex mixed with La, Ce, Pr and Nd). Urine samples from the rats dosed with HgCl2, CCl4 and Lu(NO3)(3) were collected over a 24 h time course and the samples from the rats administrated with Changle were gained after 3 months. These samples were measured by 600 MHz NMR spectroscopy. Each spectrum was data-processed to provide 223 intensity-related descriptors of spectra. Urine spectral data corresponding to the time intervals, 0-8 h (HgCl2 and CCl4), 4-8 (Lu(NO3)(3)) h and 90 d (Changle) were analyzed using principal component analysis (PCA). Successful classification of the toxicity and biochemical effects of Lu(NO3)(3) was achieved.

Peripheries of molecular projection in three-dimensional space and studies of quantitative structure-activity relationships

A new index, i.e., the periphery representation of the projection of a molecule from 3D space to a 2D plane is described. The results, correlation with toxicity of substituted nitrobenzenes, obtained by using periphery descriptors are much better than that obtained by using the areas (i.e., shadows) of projections of the compounds. Even better results were achieved by using the combination of periphery descriptors and the projections areas as well as the indicated variable K reflecting the action of group NO position on the benzene ring.

Study on QSPR of alcohols with a novel edge connectivity index F-m

A novel edge degree f(i) for heteroatom and multiple bonds in molecular graph is derived on the basis of the edge degree delta(e(r)). A novel edge connectivity index F-m is introduced. The multiple linear regression by using the edge connectivity index F-m and alcohol-type parameter delta, alcohol-distance parameter L can provide high-quality QSPR models for the normal boiling points (BPs), molar volumes (MVs), molar refraction (MRs), water solubility(log(1/S)) and octanol/water partition (logP) of alcohols with up to 17 non-hydrogen atoms. The results imply that these physical properties may be expressed as a liner combination of the edge connectivity index and alcohol-type parameter, 6, alcohol-distance parameter, L. For the models of the five properties, the correlation coefficient r and the standard errors are 0.9969,3.022; 0.9993, 1.504; 0.9992, 0.446; 0.9924,0.129 and 0.9973,0.123 for BPs, MVs, MRs, log(1/S) and logP, respectively. The cross-validation by using the leave-one-out method demonstrates the models to be highly reliable from the point of view of statistics.

Correlation analysis of the structures and stability constants of gadolinium(III) complexes

To simplify the abstraction of descriptors, for the correlation analysis of the stability constants of gadolinium(III) complexes and their ligand structures, aiming at gadolinium(III) complexes, we only considered the ligands and ignored the common parts of the structures, i.e., the metal ions. Quantum-chemical descriptors and topological indices were calculated to describe the structures of the ligands. Multiple regression analysis and neural networks were applied to construct the models between the ligands and the stability constants of gadolinium(III) complexes and satisfactory results were obtained.

Correlation analysis of the structures and pK(a) values of carboxylic acids

The quantum-chemical descriptors were used for QSPR study of the structures of carboxylic acids and their pK(a) values. The algorithm of "Leaps and Bounds" regression was performed for selection of the variables. The CoMFA method was carried out for 3D-QSPR. As the introduction of the charge of oxygen atom(Q(2)), the results obtained by CoMFA were improved greatly.

Structure-activity relationships for antiplasmodial activity among aminoquinolines

The extended gravitational index G(Q) and quantum-chemical descriptors were calculated for the relationship analysis of aminoquinolines. An evolutionary algorithm was described for variable selection and building QSAR models. And the quasi-newton neural networks were employed with better results.

Comparison of different methods for variable selection

In chemistry for chemical analysis of a multi-component sample or quantitative structure-activity/property relationship (QSAR/QSPR) studies, variable selection is a key step. In this study, comparisons between different methods were performed. These methods include three classical methods such as forward selection, backward elimination and stepwise regression; orthogonal descriptors; leaps-and-bounds regression and genetic algorithm. Thirty-five nitrobenzenes were taken as the data set. From these structures quantum chemical parameters, topological indices and indicator variable were extracted as the descriptors for the comparisons of variable selections. The interesting results have been obtained. (C) 2001 Elsevier Science B.V. All rights reserved.

Prediction of C-13-nuclear magnetic resonance chemical shifts for aliphatic amines

Prediction of C-13-nuclear magnetic resonance chemical shifts for aliphatic amines is performed. The topological, geological and electronic descriptors are generated. To reduce the variables, the best subsets of the descriptors are obtained by using leaps-and-bounds regression analysis. The model is achieved using multiple regression with satisfactory results.