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Topological Indices (topological + index)
Selected AbstractsUse of Topological Indices of Organic Sulfur Compounds in Quantitative Structure-Retention Relationship StudyMOLECULAR INFORMATICS, Issue 9 2005F. Safa Abstract Structure-gas chromatographic retention index models were developed for some organic sulfur compounds at four different temperatures (60, 80, 100 and 120,°C) using only topological descriptors. At first, regression models were generated for each temperature separately with high values of multiple correlation coefficient and Fisher-ratio statistics. The results of cross validation test using leave-one-out (Q2,0.956) and leave-two-out (Q2,0.953) methods showed good predictive ability of the models developed. Then, a single combined quantitative structure-retention relationship model, added temperature as a parameter, was also developed for all the temperatures, showing good statistical parameters (R=0.991 and F=728.474). The stability and validity of the combined model were verified by both internal (Q2>0.970) and external validation (Q=0.993) techniques. The results of the study indicated the efficiency of the classical topological descriptors in simultaneous prediction of retention index values of sulfur compounds at different temperatures. The topological descriptors well covered the molecular properties known to be relevant for gas chromatographic retention data, such as molecular size and degree of branching. [source] Accurate prediction of the blood,brain partitioning of a large set of solutes using ab initio calculations and genetic neural network modelingJOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 11 2006Bahram Hemmateenejad Abstract A genetic algorithm-based artificial neural network model has been developed for the accurate prediction of the blood,brain barrier partitioning (in logBB scale) of chemicals. A data set of 123 logBB (115 old molecules and 8 new molecules) of a diverse set of chemicals was chosen in this study. The optimum 3D geometry of the molecules was estimated by the ab initio calculations at the level of RHF/STO-3G, and consequently, different electronic descriptors were calculated for each molecule. Indeed, logP as a measure of hydrophobicity and different topological indices were also calculated. A three-layered artificial neural network with backpropagation of an error-learning algorithm was employed to process the nonlinear relationship between the calculated descriptors and logBB data. Genetic algorithm was used as a feature selection method to select the most relevant set of descriptors as the input of the network. Modeling of the logBB data by the only quantum descriptors produced a 5:4:1 ANN structure with RMS error of validation and crossvalidation equal to 0.224 and 0.227, respectively. Better nonlinear model (RMSV and RMSCV equals to 0.097 and 0.099, respectively) was obtained by the incorporation of the logP and the principal components of the topological indices to electronic descriptors. The ultimate performances of the models were obtained by the application of the models to predict the logBB of 23 molecules that did not have contribution in the steps of model development. The best model produced RMS error of prediction 0.140, and could predict about 98% of variances in the logBB data. © 2006 Wiley Periodicals, Inc. J Comput Chem 27: 1125,1135, 2006 [source] What youngsters say about adults: seedling roots reflect clonal traits of adult plantsJOURNAL OF ECOLOGY, Issue 3 2007MARIE, MILAUEROVÁ Summary 1Grime's theory on plant strategies predicts that the set of traits present in established plants is not correlated with that found in the regenerative stage of the life cycle. We tested this prediction and further investigated whether clonal growth traits, which are found in adult plants but also affect regeneration, are correlated with seedling traits. 2We characterized seedling root systems by their total length, number of root tips and several architectural parameters (length of exterior and interior root links and two topological indices). These below-ground traits were supplemented by the ratio of leaf area to root length, representing relative investment into photosynthesizing surface. We compared seedling traits with clonal growth traits, adult plant heights, and species positions on gradients of nitrogen and water availability. 3Plant species with limited horizontal spread by clonal growth exhibited a larger relative investment in photosynthetic area and also developed larger root systems as seedlings. 4Seedlings of species with taller shoots and those which occur naturally at nutrient-rich sites developed both larger roots and more dichotomously branched root systems (with higher total length and more branches). 5Taking phylogenetic inertia into account showed that this explained large parts of the variation in seedling traits. Relationships between clonal spread and seedling traits were strengthened by phylogenetic correction. 6Our study shows that some of the traits of clonal growth affect both the established and the regenerative stages of the life cycle, suggesting that an evolutionary trade-off exists between the ability to spread clonally and performance at the seedling stage. Species not able to escape from less favourable conditions by extensive clonal spread seem to be more able to exploit the location in which they germinate. [source] QSAR of Human Steroid 5,-Reductase Inhibitors: Where are the differences between isoenzyme type 1 and 2?MOLECULAR INFORMATICS, Issue 6 2004Michael Abstract Quantitative Structure Activity Relationships have been established for inhibitors of human steroid 5,-reductase including 6-azasteroids and non-steroidal compounds. From the applied descriptors, those related to the molecular geometry, electronic properties, and the electrostatic surface were derived from semi-empirical AM1 calculations. A chemical reaction as part of the inhibitory action is indicated by the presence of the ionization potential in the descriptor space. Strong similarities between the variables for the prediction of the binding affinity to the type 1 and IC50 values for the type 2 isoform of the 5,-reductase were observed. The most pronounced differences in the linear regression QSAR equations were found for the descriptors accounting for the hydrogen-bonding interaction, suggesting a different hydrogen-bonding pattern in the binding pocket of both isoforms. Furthermore, the topological indices together with the surface related descriptors point towards a lower content of aromatic amino acids in the binding site of the type 2 isoenzyme. Consequences for the design of new inhibitors are discussed. [source] Quantitative structure property relationship models for the prediction of liquid heat capacityMOLECULAR INFORMATICS, Issue 1 2003Xiaojun Yao Abstract Quantitative Structure-Property Relationship (QSPR) models based on molecular descriptors derived from molecular structures have been developed for the prediction of liquid heat capacity at 25,°C using a diverse set of 871 organic compounds. The molecular descriptors used to represent molecular structures include constitutional and topological indices and quantum chemical parameters. Forward stepwise regression and radial basis function neural networks (RBFNNs) were used to construct the QSPR models. The root mean square errors in liquid heat capacity predictions for the training, test and overall data sets are 16.857, 18.744 and 17.141 heat capacity units, respectively. The prediction results are in agreement with the experimental values, but the RBFNN model seems to be better than stepwise regression method. 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