Molecular Connectivity Model Research Articles

The depleted hydrogen atoms in chemical graph theory

A new algorithm which explicitly describes the depleted hydrogen atoms is proposed for chemical graph computations, and especially for molecular connectivity model studies. The new algorithm continues to be centred on the concepts of complete graphs and general graphs, but considers the contribution of the bonded hydrogen atoms as a kind of perturbation, which does not need any new graph concept for its formulation. The model quality of the new algorithm is tested and compared with properties, which have already been modelled without the hydrogen perturbation. Chosen classes of compounds display quite different percentages of bonded hydrogen atoms, allowing thus a checking of the contribution of the hydrogen perturbation as a function of the hydrogen content. Obtained results underline the importance of the property, and, in a minor way, the importance of the number of data points, i.e., the number of compounds, in determining the importance of the hydrogen perturbation. Molecular connectivity terms do not always show the same behaviour as the combination of basis indices relatively to the perturbation introduced by the hydrogen atoms.

The depleted hydrogen atoms in chemical graph theory

A new algorithm which explicitly describes the depleted hydrogen atoms is proposed for chemical graph computations, and especially for molecular connectivity model studies. The new algorithm contin...

Heat Capacities of 1-Chloroalkanes and 1-Bromoalkanes within the Temperature Range from 284.15 K to 353.15 K. A Group Additivity and Molecular Connectivity Analysis

The heat capacities at constant pressure of 1-chloropropane, 1-chlorobutane, 1-chloropentane, 1-chlorohexane, 1-chloroheptane, 1-chlorooctane, 1-chlorodecane, and 1-bromoalkanes (1-bromoethane, 1-bromopropane, 1-bromobutane, 1-bromopentane, 1-bromohexane, 1-bromoheptane, 1-bromononane, and 1-bromodecane) were measured within the temperature range from 284 K to 353 K by a DSC III (Setaram) differential scanning calorimeter. These data and data previously reported for α,ω-dibromoalkanes and α,ω-dichloroalkanes, have been used as input for a group additivity type analysis. The Cp group contributions as a function of temperature of the CH3 and CH2 groups and Cl and Br atoms were determined by the group additivity analysis (GAA) method. A molecular connectivity model (MCI) for the calculation of the molar heat capacities of halogenoalkanes is also presented. The given model is based on a set of six molecular connectivity indexes, MCI = {D, Dv, 0X, 0Xv, 1X, 1Xv}. The merits of the additivity scheme are discussed.

Connectivity polynomial and long-range contributions in the molecular connectivity model

A novel molecular polynomial, P χ ( G, x), representing the connectivity between all pairs of atoms in a molecule is introduced. The connectivity index χ is obtained directly from the first derivative of this polynomial, P χ ′( G, x=0). A series of descriptors containing contributions coming from non-bonded atoms in the molecule is derived by variation of x in the polynomial, P χ ′( G, x≠0). Significant improvements compared to the χ index are obtained when the novel descriptors are used in QSPR studies. The physicochemical and structural interpretations of these indices are also advanced.

Calculation of chromatographic parameters by molecular topology: sulphamides

This investigation was undertaken to test the ability of the molecular connectivity model to predict R F values in thin-layer chromatography (TLC) for a group of sulphamides using multi-variable regression equations with multiple correlation coefficients, standard error of estimate, F-Snedecor function values and Student's t-test as criteria of fit. Regression analyses showed that the molecular connectivity model predicts the values for this property in different silica gel stationary phases and different polar mobile phases. Corresponding stability and random studies were made on the selected prediction models which confirmed their goodness of fit. The results also demonstrated that different structural features determine the R F values in TLC of sulphamides.

Structure property relationships of amino acids and some dipeptides

A molecular connectivity model of the crystal densities and specific rotations of some natural amino acids and of the longitudinal relaxation rates of some natural amino acids and cyclic dipeptides is presented. While crystal densities and relaxation rates are better described by a set of three valence molecular connectivity indices {D (v),(0) X (v),(1) X (v)}, specific rotations are better described by a set of two simple molecular connectivity indices {(1) X,(0) X}. Relaxation rates are, also, well described by the simple molecular connectivity {D,(1) X} index set. Use of orthogonal indices, derived from the corresponding ordinary indices shows, in the case of specific rotations, the possibility to condense the information by the aid of a single high quality descriptor underlining, thus, the versatility of these indices and also their dependence on the orthogonalisation process.

Molecular connectivity model for determination of T1 relaxation times of α-carbons of amino acids and cyclic dipeptides

A molecular connectivity model for the calculation of the relaxation times of amino acids and cyclic dipeptides is presented. A power type regression shows the best fit between experimental and calculated data. The simple molecular connectivity index and the simple sum-delta index are the most appropriate indexes in describing the relaxation times of the investigated compounds. The given computational method shows that it is possible to model tumbling rates of solute molecules in a relatively easy and direct way.

Quantitative structure‐activity relationships for predicting the toxicity of pesticides in aquatic systems with sediment

AbstractThe toxicity of a series of organophosphorus (OP) and carbamate insecticides was measured against the midge Chironomus riparius in aquatic systems with and without sediment. Five molecular descriptors (molecular volume, Henry's law constant, n‐octanol/water partition coefficient (Kow), molecular connectivity, and linear solvation energy) were used in regression analysis as potential predictors of insecticidal activity. The regressions were conducted for each descriptor against toxicity values for the series of chemicals.Molecular volume and Henry's law constant showed no relationship with toxicity. However, log Kow was moderately successful in describing the effect of sediment on toxicity (r2 = 0.508). Prediction of toxicity was substantially improved when a linear solvation energy (LSE) or molecular connectivity (MC) model was used in regressions. In multiple regressions conducted on carbamates and OPs separately, use of MC or LSE parameters explained up to 95.8% of the variability in toxicity. Based on the results of regression analyses, sorptive interactions between these insecticides and sediment apparently dominate the processes affecting the toxicity of these compounds when sediment is present. In the absence of sediment, the regressions suggest that the molecular structure of the insecticides is more important than solubility or partitioning for determining toxicity.

Molecular connectivity model for determination of physicochemical properties of .alpha.-amino acids

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTMolecular connectivity model for determination of physicochemical properties of .alpha.-amino acidsLionello PoglianiCite this: J. Phys. Chem. 1993, 97, 25, 6731–6736Publication Date (Print):June 1, 1993Publication History Published online1 May 2002Published inissue 1 June 1993https://doi.org/10.1021/j100127a026RIGHTS & PERMISSIONSArticle Views82Altmetric-Citations30LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (503 KB) Get e-Alerts

Soil sorption and chemical topology

This study was undertaken to test the ability of the molecular connectivity model to predict the soil sorption coefficients of polar organic compounds. Quantitative models, based on the first- and second-order molecular connectivity indices, were developed for estimating soil sorption coefficients of 65 polar compounds arranged into four classes by their functional groups: acetanilides, amides, dinitroanilines, and triazoles.

Molecular Connectivity: Treatment of the Electronic Structure of Amino Acids

A molecular connectivity model of atomic charges on the second-row atoms in α-amino acids is discussed. A correlation was found between the valence delta (δv) index from molecular connectivity theory and the charges on atoms that belong to the same class, whereas different classes of carbon atoms had the same slopes on the Q versus σv matrix, where Q is the calculated charge distribution. A similar correlation was found for nucleotide bases.

Molecular topology and chromatographic retention parameters for benzodiazepines

The relationship between gas-liquid chromatographic (GLC) retention properties and R F values in thin-layer chromatography (TLC) with molecular connectivity indices, m X t, was investigated for a series of benzodiazepines using multiple correlation coefficients, standard errors of estimate, F-Snedecor function values and Student's t-test as the criteria for best equation selection. Regression analyses show that the molecular connectivity model predicts the retention properties in GLC with the polar stationary phase OV-17 at 280°C and the R F values in TLC with the stationary phase silica gel. However, zero- or second-order connectivity indices alone are not sufficient; higher-order indices are shown to be necessary. The effect of the polarity of the mobile phases in TLC was also investigated.

Relationship between gas chromatographic retention indices and molecular connectivity indices of chlorinated pesticides and structurally related compounds

The ability of the molecular connectivity model to predict retention indices using both statistical correlation coefficients and correctly predicted elution sequences as criteria of fitness was tested. The tests were performed with chlorinated pesticides and some structurally related compounds. The effect on the retention index of increasing replacement by chlorine of hydrogen atoms bonded to one carbon of the aliphatic part of the molecule was excellently correlated by 1χ v, the first-order valence molecular connectivity index, using a quadratic polynomial equation. This equation also fits 1χ v with the retention indices of halobenzenes and halobiphenyl compounds. On the other hand, 1χ v was the most significant connectivity index, using a single linear regression equation to correlate the retention indices of all the compounds. However, 1χ v alone was not sufficient to distinguish significantly some isomers and the correct elution sequence giving a relatively low correlation coefficient. Therefore, other connectivity indices and the consequently use of multiple linear regression equations were necessary in order to represent more completely the molecules and to obtain more accurate results.

Molecular Connectivity Model for Determination of Isoelectric Point of Amino Acids

A molecular connectivity model for the calculation of the isoelectric point of amino acids is presented. The model requires calculation of the molecular connectivity values of only the functional fragments of the amino acids. Multiple linear regression with two variables shows the best fit between experimental and calculated data.

Modeling plant uptake of airborne organic chemicals. 1. Plant cuticle/water partitioning and molecular connectivity

Molecular connectivity indexes were tested with regard to their ability to describe the cuticle/water partitioning for a variety of organic chemicals. A model is derived that accounts for 99% of the variation in the published cuticle/water partition coefficients (Kcw), which cover the range of 8.5 orders of magnitude. It is shown that the molecular size is directly proportional to the cuticle/water partition coefficients. It is also the most important structural feature that accounts for 89% of the variation in the measured Kcw data. Moreover, the molecular connectivity model outperforms in accuracy and speed the traditional empirical models based on l-octanol/water partition coefficients or water solubilities. Our results and their comparison with previously published models demonstrate that the molecular connectivity model is an accurate predictive tool for the cuticle/water partition coefficients of a wide range of commercial chemicals and that it can be confidently used to assess their potential to penetrate and to accumulate in aboveground vegetation.

Quantitative modeling of soil sorption for xenobiotic chemicals.

Experimentally determining soil sorption behavior of xenobiotic chemicals during the last 10 years has been costly, time-consuming, and very tedious. Since an estimated 100,000 chemicals are currently in common use and new chemicals are registered at a rate of 1000 per year, it is obvious that our human and material resources are insufficient to experimentally obtain their soil sorption data. Much work is being done to find alternative methods that will enable us to accurately and rapidly estimate the soil sorption coefficients of pesticides and other classes of organic pollutants. Empirical models, based on water solubility and n-octanol/water partition coefficients, have been proposed as alternative, accurate methods to estimate soil sorption coefficients. An analysis of the models has shown (a) low precision of water solubility and n-octanol/water partition data, (b) varieties of quantitative models describing the relationship between the soil sorption and above-mentioned properties, and (c) violations of some basic statistical laws when these quantitative models were developed. During the last 5 years considerable efforts were made to develop nonempirical models that are free of errors imminent to all models based on empirical variables. Thus far molecular topology has been shown to be the most successful structural property for describing and predicting soil sorption coefficients. The first-order molecular connectivity index was demonstrated to correlate extremely well with the soil sorption coefficients of polycyclic aromatic hydrocarbons (PAHs), alkylbenzenes, chlorobenzenes, chlorinated alkanes and alkenes, heterocyclic and heterosubstituted PAHs, and halogenated phenols. The average difference between predicted and observed soil sorption coefficients is only 0.2 on the logarithmic scale (corresponding to a factor of 1.5). A comparison of the molecular connectivity model with the empirical models described earlier shows that the former is superior in accuracy, performance, and range of applicability. It is possible to extend this model, with the addition of a single, semiempirical variable, to take care of polar and ionic compounds and to accurately predict the soil sorption coefficients for almost 95% of all organic chemicals whose coefficients have been reported. No empirical or nonempirical models have ever predicted the soil sorption coefficients to such a high degree of accuracy on such a broad selection of structurally diverse compounds. An additional advantage of the molecular connectivity model is that it is sufficient to know the structural formulas to make predictions about soil sorption coefficients.(ABSTRACT TRUNCATED AT 400 WORDS)

Quantitative structure-activity relationships of acute toxicity of commercial chemicals on fathead minnows: effect of molecular size

This study was undertaken to test the ability of the molecular connectivity model to predict the level of acute toxicity of alcohols, ethers, aldehydes, ketones, nitriles, aliphatic and aromatic amines, halogenated hydrocarbons, substituted benzenes, and phenols for fish. We obtained good linear correlation between the valence zero-order molecular connectivity index ( 0 χ v ) and 96-h LC 50 data for a large group of commercial chemicals. Our results and their comparison with previously published models demonstrate that the molecular connectivity model is an accurate predictive tool for assessing the level of acute toxicity of a wide range of commercial chemicals. It can be confidently used to rank potentially hazardous chemicals and to create priority testing lists. In addition, it is shown that the molecular size of chemicals is directly proportional to the fish acute toxicity and that is the most important structural feature that accounts for 85% of the variation in the measured fish acute toxicity data. Finally, an attempt was made to critically evaluate and compare all published QSAR models for predicting acute toxicity for fish.

Calculation of Molecular Volumes from Molecular Fragments via Valence Electron Indices

AbstractIt is shown that van der Waals volumes of 1‐atomic fragments, c.q. hydrides, can be predicted accurately via indices denoting the number of non‐hydrogen valence electrons. The introduction of a principal quantum number dependence of orbital volumes turned out to be necessary, which led to a modified molecular connectivity model. From these fragmental indices molecular valence electron indices were derived, which are demonstrated to be accurate predictors of the molecular volume of polycyclic aromatic compounds in an additive model.

On the prediction of soil sorption coefficients of organic pollutants from molecular structure: application of molecular topology model

This study was undertaken to test the ability of the molecular connectivity model to predict the soil sorption coefficients of polycyclic aromatic hydrocarbons (PAHs), alkylbenzenes, chlorobenzenes, chlorinated alkanes and alkenes, heterocyclic and substituted PAHs, and halogenated phenols. Tests performed on 31 such compounds clearly demonstrate that this simple nonempirical model accurately predicts the soil sorption coefficients for the above classes of compounds. Moreover, the model out performs the traditional empirical models based on 1-octanol/water partition coefficients or water solubilities in accuracy, speed, and range of applicability. Additional tests on approximately 160 polar and ionic compounds show that the above topological model plus a single semiempirical variable is capable of accounting for the soil sorption properties of nearly 95% of all organic chemicals whose soil sorption coefficients have been measured. The results of these tests demonstrate that the molecular connectivity model is a very accurate predictive tool for the soil sorption coefficients of a wide range of organic chemicals and that it can be confidently used to rank potentially hazardous chemicals and thus to create a priority testing list. 55 references, 5 figures, 7 tables.

Calculation of retention indices by molecular topology: Chlorinated benzenes

A comparative study was undertaken to test the ability of several different topological indices to predict the retention indices of chlorinated benzenes on polar and non-polar stationary phases using both correlation coefficients and correctly predicted elution sequences as criteria of fit. The test was performed on three topological indices: connectivity indices, Wiener numbers, and Balaban indices. The regression analyses showed that the molecular connectivity model predicted the retention indices of chlorinated benzenes more successfully than either Wiener numbers or Balaban indices. The results also demonstrated that the major structural property controlling chromatographic behavior was the size of the chlorinated benzene. In addition, the use of the new non-empirical heteroatom parameterization scheme in the calculation of Wiener numbers and Balaban indices was successfully tested for the first time.