Number Of Single Bonds Research Articles

Bulk and interfacial liquid water as a transient network.

The special macroscopic properties of liquid water stem from its structure as a complex network of molecules connected by hydrogen bonds. While the dynamics of single molecules within this network has been extensively investigated, only little attention has been paid to the closed loops (meshes) of hydrogen-bonded molecules which determine the network topology. Using molecular dynamics simulations we analyze the size, shape, geometrical arrangement, and dynamical stability of loops containing up to 10 hydrogen bonds. We find that six-membered loops in liquid water even at room temperature retain a striking similarity with the well-known structure of ice. Analyzing the network dynamics we find that rings of more than five hydrogen bonds are stabilized compared to a random collection containing the same number of single bonds. We finally show that in the vicinity of hydrophobic and hydrophilic interfaces loops arrange in a preferred orientation.

Importance of structural information in predicting human acute toxicity from in vitro cytotoxicity data

In this study, we tried to assess the utility of the structural information of drugs for predicting human acute toxicity from in vitro basal cytotoxicity, and to interpret the informative quality and the pharmacokinetic meaning of each structural descriptor. For this, human acute toxicity data of 67 drugs were taken from literature with their basal cytotoxicity data, and used to develop predictive models. A series of multiple linear regression analyses were performed to construct feasible regression models by combining molecular descriptors and cytotoxicity data. We found that although the molecular descriptors alone had only moderate correlation with human acute toxicity, they were highly useful for explaining the discrepancy between in vitro cytotoxicity and human acute toxicity. Among many possible models, we selected the most explanatory models by changing the number and the type of combined molecular descriptors. The results showed that our selected models had high predictive power ( R 2: between 0.7 and 0.87). Our analysis indicated that those successful models increased the prediction accuracies by providing the information on human pharmacokinetic parameters which are the major reason for the difference between human acute toxicity and cytotoxicity. In addition, we performed a clustering analysis on selected molecular descriptors to assess their informative qualities. The results indicated that the number of single bonds, the number of hydrogen bond donors and valence connectivity indices are closely related to linking cytotoxicity to acute toxicity, which provides insightful explanation about human toxicity beyond cytotoxicity.

Empirical prediction of peptide octanol-water partition coefficients

Peptides are of great therapeutic potential as vaccines and drugs. Knowledge of physicochemical descriptors, including the partition coefficient P (commonly expressed in logarithm form: logP), is useful for screening out unsuitable molecules and also for the development of predictive Quantitative Structure-Activity Relationships (QSARs). In this paper we develop a new approach to the prediction of LogP values for peptides based on an empirical relationship between global molecular properties and measured physical properties. Our method was successful in terms of peptide prediction (total r(2) = 0.641). The final model consisted of 5 physicochemical descriptors (molecular weight, number of single bonds, 2D-VDW volume, 2D-VSA hydrophobic and 2D-VSA polar). The approach is peptide specific and its predictive accuracy was high. Overall, 67% of the peptides were able to be predicted within +/-0.5 log units from the experimental values. Our method thus represents a novel prediction method with proven predictive ability.

Prediction of ozone tropospheric degradation rate constant of organic compounds by using artificial neural networks

Ozone tropospheric degradation of organic compound is very important in environmental chemistry. The lifetime of organic chemicals in the atmosphere can be calculated from the knowledge of the rate constant of their reaction with free radicals such as OH and NO 3 or O 3. In the present work, the rate constant for the tropospheric degradation of 137 organic compounds by reaction with ozone, the least widely and successfully modeled degradation process, are predicted by quantitative structure activity relationships modeling based on a variety of theoretical descriptors, which screened and selected by genetic algorithm variable subset selection procedure. These descriptors which can be used as inputs for generated artificial neural networks are; HOMO–LUMO gap, number of double bonds, number of single bonds, maximum net charge on C atom, minimum (>0.1) bond order of C atom and Minimum e–e repulsion of H atom. After generation, optimization and training of artificial neural network, network was used for the prediction of log KO 3 for the validation set. The root mean square error for the neural network calculated log KO 3 for training, prediction and validation set are 0.357, 0.460 and 0.481, respectively, which are smaller than those obtained by multiple linear regressions model (1.217, 0.870 and 0.968, respectively). Results obtained reveal the reliability and good predictivity of neural network model for the prediction of ozone tropospheric degradations rate constant of organic compounds.

Prediction of Retention Factors in Supercritical Fluid Chromatography Using Artificial Neural Network

In this study, a quantitative structure-property relationship technique has been used for the prediction of retention factors for some organic compounds in supercritical fluid chromatography using an artificial neural network. The best descriptors that appear in this model are the number of single bonds, the number of double bonds, and the hydrophilic factor. These descriptors were used as inputs for a generated artificial neural network. This network has a 3 : 3 : 1 topology that was trained using a back-propagation algorithm. The cross-validation method was used to evaluate the predictive power of the generated network. The results obtained by artificial neural networks were compared with the experimental values as well as with those obtained using the multiple linear regression technique. Comparison of these results shows the ability of the artificial neural network model to predict retention factors.

Stabilities of Hydrogen-Bonded Supramolecular Complexes with Various Numbers of Single Bonds: Attempts To Quantify a Dogma in Host-Guest Chemistry

The disadvantage of flexible bonds in supramolecular host-guest complexes is much smaller than usually assumed. In the association of dicarboxlic acids with diamides (shown on the right), freely rotatable single bonds have only a minor disadvantageous influence on the free energy of complexation ΔGcplx . From the linear correlation between ΔGcplx and the number of single bonds n, a decrease in the free energy of complexation of only 1.3 kJ mol-1 per single bond was determined.

The Change in the Raman Spectra of Chloroprene and Isoprene in the Polymerization Process

Abstract 1. When chloroprene and isoprene polymerize, besides the frequency characterizing the conjugate double bond in the monomer, there appears a higher frequency corresponding to the isolated double bond in the polymer. In the polymerization process, the intensity of the frequency of the conjugate double bond decreases and the intensity of the frequency of the isolated double bond increases. Because of the increase in the number of single bonds in the polymer, the intensity of the frequency of the single bond 1005 in the polymer is considerably greater than in the monomer. 2. Even in the case of the samples with high polymer contents (greater than 50 per cent), the intensity of the frequency of the conjugate double bond is considerably greater than the intensity of the frequency of the isolated double bond. This is attributable to the fact that part of double bonds disappear during polymerization. 3. The Raman spectra of the chloroprene and isoprene polymers differ essentially from those of the monomers. To characterize the frequencies of vibration in the polymer molecule, it is essential to investigate its Raman spectrum in a medium free of the monomer. 4. The formation of highly polymeric molecules on polymerization does not result in an increase in the intensity of the continuous background in spectrograms.