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 structure-property relationship research to the analysis of photoionization sensitivity of organic compounds in gas chromatography

The A(m) index and molecular connectivity index were used for studying the photoionization sensitivity of some organic compounds in gas chromatography. The analysis of structure-property relationship between the photoionization sensitivity of the compounds and the A(m) indices or molecular connectivity indices has been carried out. The genetic algorighm was used to build the correlation model in this field. The results demonstrate that the property of compounds can be described by both A(m) indices and molecular connectivity indices, and the mathematical model obtained by the genetic algorithm was better than that by multivariate regression analysis.

Machine learning methods for quantitative structure-property relationship modeling

Tese de doutoramento, Informática (Bioinformática), Universidade de Lisboa, Faculdade de Ciências, 2014

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.

Atomistic investigation on the structure-property relationship during thermal spray nanoparticle impact

During thermal spraying, hot particles impact on a colder substrate. This interaction of crystalline copper nanoparticles and copper substrate is modelled, using MD simulation. The quantitative results of the impacts at different velocities and temperatures are evaluated using a newly defined flattening aspect ratio. This ratio between the maximum diameter after the impact and the height of the splat increases with increasing Reynolds numbers until a critical value is reached. At higher Reynolds numbers the flattening aspect ratio decreases again, as the kinetic energy of the particle leads to increasing substrate temperature and, therefore, decreases the substrate resistance. Thus, the particle penetrates into the substrate and deforms less.

Quantitative structure-property relationships for octanol-air partition coefficients of polychlorinated naphthalenes, chlorobenzenes and p,p '-DDT

The octanol-air partition coefficient (K-OA) is a key descriptor of chemicals partitioning between the atmosphere and environmental organic phases. Quantitative structure-property relationships (QSPR) are necessary to model and predict KOA from molecular structures. Based on 12 quantum chemical descriptors computed by the PM3 Hamiltonian, using partial least squares (PLS) analysis, a QSPR model for logarithms of K-OA to base 10 (log K-OA) for polychlorinated naphthalenes (PCNs), chlorobenzenes and p,p'-DDT was obtained. The cross-validated Q(cum)(2) value of the model is 0.973, indicating a good predictive ability of the model. The main factors governing log K-OA of the PCNs, chlorobenzenes, and p,p'-DDT are, in order of decreasing importance, molecular size and molecular ability of donating/accepting electrons to participate in intermolecular interactions. The intermolecular dispersive interactions play a leading role in governing log K-OA. The more chlorines in PCN and chlorobenzene molecules, the greater the log K-OA values. Increasing E-LUMO (the energy of the lowest unoccupied molecular orbital) of the molecules leads to decreasing log K-OA values, implying possible intermolecular interactions between the molecules under study and octanol 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.

Applications of the method of three-dimensional projection of a molecule in quantitative structure-activity relationship of phenol derivatives

Five variables for phenol derivatives were calculated by molecular projection in three-dimensional space which were combined with eight quantum-chemical parameters and three Am indices. These variables were selected by using leaps-and-bounds regression analysis. Multiple linear regression analysis and artificial neural networks' were performed, and the results obtained by using. artificial neural networks are superior than that obtained by using multiple linear regression.

Applications of generalized torsion angles in studies on quantitative structure-activity relationship

In this article, generalized torsion angles of derivatives of 1-[(2-hydroxyethoxy)methy1]-6(phenylthio)thymine(HEPT) were calculated, which include abundant three dimensional information of molecules. Molecular similarity matrix was built based on the calculated generalized torsion angles. These similarities were taken as the new variables, and the new variables were selected by using Leaps-and-Bounds regression analysis. Multiple regression analysis and neural networks were performed, and the satisfactory results were achieved by using the neural networks.

Effective descriptions of molecular structures and the quantitative structure - Activity relationship studies

In this research. we found CoMFA alone could not obtain sufficiently a strong equation to allow confident prediction for aminobenzenes. When some other parameter. such as heat of molecular formation of the compounds, was introduced into the CoMFA model, the results Were improved greatly. It gives us a hint that a better description for molecular structures will yield a better prediction model, and this hint challenged us to look for another method-the projection areas of molecules in 3D space for 3D-QSAR. It is surprising that much better results than that obtained by using CoMFA Were achieved. Besides the CoMFA analysis. multiregression analysis and neural network methods for building the models were used in this paper.