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Katritzky A.R.,University of Florida | Kuanar M.,University of Florida | Slavov S.,University of Florida | Hall C.D.,University of Florida | And 4 more authors.
Chemical Reviews | Year: 2010

A study was conducted to demonstrate quantitative correlation of physical and chemical properties with chemical structure. It was demonstrated that the establishment of quantitative correlations between diverse molecular properties and chemical structure was essential to assess and improve environmental, medicinal, and technological aspects of life. These were expressed as quantitative structure-property relationships (QSPR) that related physical, chemical, or physicochemical properties of compounds to their structures. A significant objective of the QSPR studies was to find a mathematical relationship between the property of interest and one or more descriptive parameters derived from the structure of the molecule. The descriptors used in the study included experimental properties or properties derived from readily available experimental characteristics of the structure or computed based on the structure. Source


Katritzky A.R.,University of Florida | Kasemets K.,University of Florida | Kasemets K.,MolCode Ltd. | Kasemets K.,University of Tartu | And 8 more authors.
Water Research | Year: 2010

The experimental logEC50 toxicity values of 104 compounds causing bioluminescent repression of the bacterium strain Pseudomonas isolated from an industrial wastewater were studied. Using the Best Multilinear Regression method implemented in CODESSA PRO, models with up to 8 theoretical descriptors were obtained. Utilizing a rigorous descriptor selection and validation procedure a reliable QSAR model with four parameters was selected as best. The proposed model emphasizes the importance of the halogen atoms presented in each compound, the possibility of H-bond formation and the flexibility and degree of branching of the molecules. As pointed out by many researchers, the contribution of the octanol-water partition coefficient to the explanation of the toxicity effect was also found to be significant. In addition, the model currently proposed was compared to those reported earlier and its advantages were discussed in detail. © 2010 Elsevier Ltd. Source


Katritzky A.R.,University of Florida | Radzvilovits M.,University of Florida | Radzvilovits M.,MolCode Ltd. | Radzvilovits M.,University of Tartu | And 8 more authors.
Toxicological and Environmental Chemistry | Year: 2010

The bioconcentration factors (BCFs) of 57 polychlorinated biphenyl (PCB) congeners were modeled by quantitative structure-activity relationship (QSAR) based on 486 constitutional, topological, geometrical, electro- static, quantum chemical, and thermodynamic descriptors derived solely from molecular structure and calculated using CODESSA Pro (comprehensive descriptors for structural and statistical analysis) software. Descriptors utilized for the general model were selected by various statistical validation techniques. Multilinear models were developed using the best multilinear regression algorithm to relate experimental BCF to a set of molecular descriptors. The proposed two-parameter model satisfactorily describes the relationship between observed and calculated values in terms of statistical parameters. Polarity, structural flexibility, and spatial mass distribution of a molecule were demonstrated to be the main factors influencing the ability of PCB to (1) penetrate through lipophilic cell membranes and (2) bind specifically and nonspecifically to biological targets, hence affecting BCF. Comparison to other models indicated advantages of the proposed model over previously reported ones. Derived two-parameter regression equation has improved statistics and is based on theoretical descriptors with a definite physicochemical meaning; it is easier to use and interpret due to the mathematical simplicity of the linear QSAR approach. Internal validation and scrambling procedure confirmed the stability and reliable predictive ability of the general model and indicated the absence of chance correlations. External validation demonstrated that the presented model can be applied to structurally similar sets of compounds, thus extending the domain of applicability of the model. © 2010 Taylor & Francis. Source


Dobchev D.A.,Tallinn University of Technology | Dobchev D.A.,MolCode Ltd. | Mager I.,University of Tartu | Mager I.,University of Stockholm | And 11 more authors.
Current Computer-Aided Drug Design | Year: 2010

An investigation of cell-penetrating peptides (CPPs) by using combination of Artificial Neural Networks (ANN) and Principle Component Analysis (PCA) revealed that the penetration capability (penetrating/non-penetrating) of 101 examined peptides can be predicted with accuracy of 80%-100%. The inputs of the ANN are the main characteristics classifying the penetration. These molecular characteristics (descriptors) were calculated for each peptide and they provide bio-chemical insights for the criteria of penetration. Deeper analysis of the PCA results also showed clear clusterization of the peptides according to their molecular features. © 2010 Bentham Science Publishers Ltd. Source

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