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.

Studies on three-dimensional quantitative structure-activity relationships between the structures of N-nitroso compounds and their carcinogenic activities

Comparative molecular fiels analysis (CoMFA) has been applied to the studies of the correlation of the N-nitroso compounds and their carcinogenic activities, The comparison of CoMFA results with different lattice spacing and different atom probes was investigated, CoMFA resulted in a quantitative description of the major steric and electrostatic field effects and gave significant new insights to factors governing potency.

QUANTITATIVE STRUCTURE-ACTIVITY-RELATIONSHIPS FOR TOXICITY OF PHENOLS USING REGRESSION-ANALYSIS AND COMPUTATIONAL NEURAL NETWORKS

Quantitative structure-toxicity models were developed that directly link the molecular structures of a et of 50 alkYlated and/or halogenated phenols with their polar narcosis toxicity, expressed as the negative logarithm of the IGC50 (50% growth inhibitor

Quantitative structure-property relationships for colour reagents and their colour reactions with cerium using computational neural networks

Quantitative structure-activity/property relationships (QSAR/QSPR) studies have been exploited extensively in the designs of drugs and pesticides, but few such studies have been applied to the design of colour reagents. In this work, the topological indices A(x1)-A(x3) suggested in this laboratory were applied to multivariate analysis in structure-property studies. The topological indices of 43 phosphone bisazo derivatives of chromotropic acid were calculated. The structure-property relationships between colour reagents and their colour reactions with cerium were studied using A(x1-Ax3) indices with satisfactory results. The purpose of this work was to establish whether QSAR can be used to predict the contrasts of colour reactions and in the longer term to be a helpful tool in colour reagent design.

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.

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.

Selection for variables in quantitative structure-activity/property relationship studies - Comparison between methods of orthogonal descriptors and leaps-and-bounds regression analysis

In this paper, the comparison of orthogonal descriptors and Leaps-and-Bounds regression analysis is performed. The results obtained by using orthogonal descriptors are better than that obtained by using Leaps-and-Bounds regression for the data set of nitrobenzenes used in this study. Leaps-and-Bounds regression can be used effectively for selection of variables in quantitative structure-activity/property relationship(QSAR/QSPR) studies. Consequently, orthogonalisation of descriptors is also a good method for variable selection for studies on QSAR/QSPR.

Studies on quantitative structure-activity relationship between the structures of derivatives of benzamide and their antiallergic activities

Molecular connectivity index and comparative molecular field analysis (CoMFA) have been applied to the studies of the correlation of the derivatives of benzamide and their antiallergic activities. The results achieved by using CoMFA based on 3D factors are much better than that obtained by using multiple regression analysis based on majorly 2D structural information. The CoMFA results show that the dominant factor which affects activity is steric, whereas electrostatic effect only plays an unimportant role.

Quantitative structure-property relationships for colour reagents and their colour reactions with ytterbium using regression analysis and computational neural networks

In this paper, the new topological indices A(x1)-A(x3) suggested in our laboratory and molecular connectivity indices have been applied to multivariate analysis in structure-property studies. The topological indices of twenty asymmetrical phosphono bisazo derivatives of chromotropic acid have been calculated. The structure-property relationships between colour reagents and their colour reactions with ytterbium have been studied by A(x1)-A(x3) indices and molecular connectivity indices with satisfactory results. Multiple regression analysis and neural networks were employed simultaneously in this study.

STUDY ON STRUCTURE-ACTIVITY-RELATIONSHIPS OF ORGANIC-COMPOUNDS - 3 NEW TOPOLOGICAL INDEXES AND THEIR APPLICATIONS

In this paper, three new topological indices, A(x1), A(x2), and A(x3), have been developed for use in multivariate analysis in structure-property relationship (SPR) and structure-activity relationship (SAR) studies. Good results have been obtained by using them to predict the physical and chemical properties and biological activities of some organic compounds.

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.

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.