Electrotopological State Indices Research Articles

Prediction of migration behaviour of flavonoids in capillary zone electrophoresis by means of topological indices

Topological indices of 13 flavonoids were calculated to represent the molecular structures. The electrophoretic mobilities ( μ e) of the flavonoids were obtained to represent the migration in capillary zone electrophoresis (CZE). Then multiple linear regression techniques were used to establish the models for predicting the mobilities by means of the topological indices. 23 models of two-indices were generated with similar statistical results ( R≥0.93, n=13, relative standard error≤10%). Each model developed was based on a molecular connectivity index ( m χ t ν) and the other electrotopological state index ( S i ). The m χ t ν and S i involved in models can reasonably explain the μ e. Thus, the results demonstrate that m χ t ν and S i can successfully be used to develop models of structure–mobility of flavonoids in CZE. The structural factors affecting observed and predicted μ e were discussed.

The Statistical Evaluation of Migration Parameters of Flavonoids in Capillary Electrophoresis with Reference to Structural Descriptors

Under each of 38 buffer conditions, six migration parameters of 14 flavonoids were obtained in capillary zone electrophoresis (CZE) and nine parameters in micellar electrokinetic capillary chromatography (MEKC). A total of 183 structural descriptors for each flavonoid were calculated to represent the molecular structures in a quantitative way. The relationship between the structural descriptors and the migration parameters of flavonoids in CZE or MEKC were evaluated by cluster and factor analyses. Under all CZE conditions the apparent solute mobility (μa) was always the first variable to associate with all molecule-level structural descriptors and the electrotopological state indices (Si) of skeletal carbons, whereas the effective mobility (μe) was closer to Si of oxygens on skeletons. On the other hand, in MEKC retention factor or its logarithm did not depended more on the structural descriptors than other migration parameters. This indicated that the parameter did not represent the properties of the flavonoids better than other parameters. Thus, the results confirmed that hydrophobic interaction was not the only underlying force that influenced the migration behaviour of the flavonoids in MEKC.

QSAR modeling with the electrotopological state indices: corticosteroids.

A structure-activity analysis of a series of steroids binding to corticosteroid-binding globulin was made using the electrotopological state index for each atom in the molecule. Two indices were found to correlate well with the binding affinity. The indices encode structural characteristics in the A and the D rings of the steroids in the study. One of the indices was formulated as the difference between two indices in the A ring. The two were not intercorrelated, suggesting that the composite index signals the influence of structure changes in or near the A ring that can be monitored by the composite index. This is a new observation using this structure-activity method. It is suggested that this model makes some contributions towards detection of the pharmacophore.

Boiling Point of a Set of Alkanes, Alcohols and Chloroalkanes: QSAR with Atom Type Electrotopological State Indices Using Artificial Neural Networks

The normal boiling point is modeled for a set of 372 saturated compounds, including 154 alkanes, 108 alcohols and 110 (poly)chloroalkanes. The newly introduced atom type electrotopological state indices serve as the structure variables and artificial neural networks (with back propagation of error) are used for the analysis. A network with a 6:7:1 architecture produces an average relative error of 0.97% for the whole data set, including the 21% of the data used as the test set. The mean absolute error (MAE) for this model is 4.00 for the whole set, corresponding to an rms error of 5.41; for the test set the MAE is 4.03 with an rms of 5.23. The low error on the test set indicates that this model has predictive power.

The E-State as an Extended Free Valence

The relationship between the electrotopological state index (E-state) and the molecular orbital based index of free valence is examined. The dependence of both on electronegativity and topology are developed from comparative values. Calculations of both indices for a diverse set of conjugated carbons reveal a significant correlation. Since the E-state index is calculated for heteroatoms and all σ bonded atoms, it represents an extention of the concept of free valence applied to all atoms in a molecule.

Neural Network Modeling for Estimation of the Aqueous Solubility of Structurally Related Drugs

The ability of neural network models to predict aqueous solubility within series of structurally related drugs was evaluated. Three sets of compounds representing different drug classes (28 steroids, 31 barbituric acid derivatives, and 24 heterocyclic reverse transcriptase inhibitors) were studied. Topological descriptors (connectivity indices, kappa indices, and electrotopological state indices) were used to link the structures of compounds with their aqueous solubility. Separate models were built for each class of drugs using back-propagation neural networks with one hidden layer and five topological indices as input parameters. The effect of network size and training time on the prediction ability of the network was studied by the leave-one-out (LOO) procedure. In all three compound groups a neural network structure of 5-3-1 was optimal. To avoid chance effects, artificial neural network (ANN) ensembles (i.e.; averaging neural network predictions over several independent networks) were used. The cross-validated squared correlation coefficient (Q2) for 10 averaged predictions was 0.796 in the case of the steroid set. The corresponding standard error of prediction (SDEP) was 0.288 log units. For the barbiturates, Q2 and SDEP were 0.856 and 0.383, respectively, and for the RT inhibitors, these parameters were 0.721 and 0.401, respectively. The results indicate that neural networks can produce useful models of the aqueous solubility of a congeneric set of compounds, even with simple structural parameters.

QSARs with Orthogonal Descriptors on Psychotomimetic Phenylalkylamines

AbstractMultiple linear regressions have been obtained for 49 psychotomimetic phenylalkylamines. The MTD method has been applied and the MTD parameter was used as steric descriptor. The energy of the lowest unoccupied molecular orbital and the net charges on the phenyl ring atoms (from AMI calculations) have been used as electronic descriptors and logPow, or average electrostatic field as lipophilicity descriptor. Hall and Kier electrotopological state indices were also tested. The descriptors have been scaled to unit and orthogonalized by Randic method. The predictability of the regressions obtained from MTD method have been checked by a method similar to cross‐validation from CoMFA method. MTD has been identified as the dominant descriptor by Randic orthogonalization. The best model has been obtained with MTD and electrotopological state indices, showing the importance of the steric and electronic properties for the interaction at the receptor site level.

Using topological indices in the prediction of gas chromatographic retention indices of linear alkylbenzene isomers

Abstract The Balaban, Wiener, Electrotopological state and molecular shape indices were determined and correlated with the retention indices of linear alkylbenzene isomers with C 10 -C 14 linear alkyl chains. The Wiener index is the only one which shows a good single linear correlation with the retention indices and a correct elution sequence. Multiple linear regression analysis with the connectivity index ( 1 ξ ) and electrotopological state index ( S ) for the C-7 position of the linear alkyl chain, which encode complementary information, also gives a good correlation coefficient and a good elution sequence (it is not correct only for four compounds). The different orders of shape indeces (kappa values), indices of molecular shape, also give a good correlation by a multiple linear regression, the best correlation coefficient being obtained with the 1 κ , 6 κ indices, and the 1 κ , 7 κ indices (the elution sequence is not correct for five compounds).

Application of the electrotopological state index to QSAR analysis of flavone derivatives as HIV-1 integrase inhibitors.

A QSAR study based on electrotopological state (E-state) indices was conducted for a series of flavone HIV-1 integrase inhibitors to guide drug design. E-state indices formulated to encode electronic and topological information for each skeletal atom in a molecule (Kier and Hall Pharm. Res. 7:801-807 (1990)) were calculated using the Molconn-X program, and partial least squares (PLS) multivariate regression was used to derive QSAR models. Predictive models with correlation coefficients (r2) of 0.98 (3 PLS components) and 0.99 (5 PLS components) and corresponding cross-validated correlation coefficients (c.v. r2) of 0.51 and 0.73, were obtained for inhibition of cleavage and integration, respectively, with one molecule omitted from the analysis. E-state indices at C6, C3', C5', C5, and O4 were found to be more important for prediction of activity than those for any of the other 12 flavone skeletal atoms that are common to the molecules in the data set.

Boiling Point and Critical Temperature of a Heterogeneous Data Set: QSAR with Atom Type Electrotopological State Indices Using Artificial Neural Networks

Two sets of heterogeneous organic compounds were analyzed with artificial neural networks using atom type electrotopological state indices. The first set contains the boiling point for 298 compounds; 30 were placed in a testing set. The neural network model used atom type E-state indices for the 19 atom types present in the data set; the actual network used for prediction had a 19:5:1 architecture. This model produced a mean absolute error (MAE) of 3.93 K for the overall set, 3.86 for the training set, and 4.57 for the test set. The average relative percent error for 10 runs is 0.94% for the whole data set and 1.12% for the test set. The second set contains critical temperatures for 165 compounds; 18 were placed in the testing set. The neural network possessed a 19:4:1 architecture and produced an MAE of 4.52 K for the whole set, 4.39 K for the training set, and 5.59 K for the test set. The average relative percent error for 5 runs is 0.77% for the whole data set and 0.95% for the test set.

Electrotopological State Indices for Atom Types: A Novel Combination of Electronic, Topological, and Valence State Information

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTElectrotopological State Indices for Atom Types: A Novel Combination of Electronic, Topological, and Valence State InformationLowell H. Hall and Lemont B. KierCite this: J. Chem. Inf. Comput. Sci. 1995, 35, 6, 1039–1045Publication Date (Print):November 1, 1995Publication History Published online1 May 2002Published inissue 1 November 1995https://doi.org/10.1021/ci00028a014RIGHTS & PERMISSIONSArticle Views2496Altmetric-Citations479LEARN 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 (775 KB) Get e-Alerts

Artificial neural networks applied to classification of mutagenic activity of nitro-substituted polycyclic aromatic hydrocarbons

Three-layer artificial neural networks (ANN) with back-propagation of error have been applied to classification of nitro-substituted polycyclic aromatic hydrocarbons (NPAH) based on the regularity that the structure difference of NPAH compounds leads to different mutagenic activity towards Salmonella typhimurium. The network's architecture and parameters were optimized to give maximum correct classification rate of 93.8% for two different classes of NPAHs: weakly active and strongly active ones. The results compared favourably with those obtained by nonlinear mapping pattern recognition method. Considering that the most important factor for NPAH's mutagenicity might be the electron effect of the substituted nitro-groups, an electrotopological state index of nitrogen atom was introduced additionally as one of network's inputs, and the correct classification rate was consequently raised to 97.5%. The network's prediction ability for untrained samples was also satisfactory.

Statistical evaluation of molecular descriptors and quantitative structure-property relationship studies of retinoids.

Various molecular descriptors, including connectivity indices, sums of the intrinsic state values, electrotopological state indices, topological equivalence indices, kappa indices, normalized Bonchev-Trinajstic and Shannon information indices, Wiener and Platt's F numbers and molecular weight, were calculated for 73 retinoids, whose structures and properties were taken from the literature. A novel methodology using statistical analyses (cluster, factor and stepwise regression) in selecting relevant molecular descriptors for quantitative structure-property relationship (QSPR) studies has been developed. The analyses were used in correlating molecular structure with affinity, pharmacokinetic properties and reversed-phase retention of retinoids.

A QSAR Study of Quinolones Based on Electrotopological State Index for Atoms

AbstractA series of 18 quinolone antibacterials were used in a quantitative structure‐activity relationship (QSAR) study based on electrotopological state (E‐state) index for atoms. The results were correlated with minimum inhibitory concentrations (MICs) against E. coli H560 and the DNA gyrase cleavage values. The QSAR equations indicated that the in vitro antibacterial activity was strongly dependent on the E‐state indexes for the atom at position 6 and the O(sp2) of the carbonyl and carboxyl groups. This suggests the possibility of an electrostatic interaction between the atom at position 6 and the receptor; as well as the formation of hydrogen bonds between the carbonyl groups of the quinolone and the receptor.

Comparison of Electrotopological State Indexes with Molecular Orbital Parameters: Inhibition of MAO by Hydrazides

AbstractQSAR models for inhibition of monoamine oxidase (MAO) by aryloxyacetohydrazide derivatives were developed for a series of MO parameters computed with the AM1 Hamiltonian. These models were compared to those based on the recently developed electrotopological state index for. skeletal atoms. The structure interpretation is the same for both types of models: the NH group adjacent to the carbonyl group together with the carbon atom, gamma to the NH group, in the hydrocarbon substituent, account for more than 90% of the computed activity. However, the model based on E‐state indexes (r2 = 0.9012) is significantly superior to the one based on MO parameters (r2 = 0.7985); the standard deviation for the MO based model (s = 0.27) is 50% larger than that from the E‐state based model (r = 0.19). Several points of comparison between the models are presented. This study supports the idea that the electrotopological state is a useful representation of molecular structure information.

A quantitative structure-activity relationship (QSAR) study of alkylpyrazine odor modalities.

The odor strength of a series of alkylpyrazines has been quantitatively investigated applying molecular connectivity, molecular shape, and the recently developed electrotopological state indices. The successful use of the latter parameters indicates that both electronic and topological features contribute to the odor strength of the compounds under study, while the specific role of the two nitrogen atoms is revealed.

The use of electrotopological state indices in QSAR studies

The development and the interpretation of a non-empirical, atom- centered molecular descriptor the electrotopological state (E-state) have been discussed. This descriptor encodes information about the electronic and topological nature of atoms in molecules. The E-state values are seen in QSAR equations to predict the odor sensitivity of pyrazines and the anesthetic concentration of haloalkanes.

The Electrotopological State: An Atom Index for QSAR

AbstractA new characterization of atoms in molecules is introduced as the electrotopological state index, which combines both the electronic character and the topological environment of each skeletal atom in a molecule. The electrotopological state (E‐state) of a skeletal atom is formulated as an intrinsic value Ii plus a perturbation term ΔIi, arising from the electronic interaction within the molecular topological environment of each atom in the molecule. The atom intrinsic value, for first row atoms, is expressed as I = (δv + 1)δ, in which δv and δ are the counts of valence and sigma electrons, respectively, for the atom in the molecular skeleton. The E‐state, Si, for atom i is defined as Si = Ij + ΔIj, where the influence of other atoms on atom i, ΔIi, is given as Σ(Ij — Ij)/rij2; rij is the graph separation between atoms i and j, counted as number of atoms, including i and j. Information in the electrotopological state is revealed by examples of various types of organic structures, including skeletal branching and heteroatom variation. The relation of the E‐state value to NMR chemical shift is demonstrated for a series of carbonyl compounds. QSAR examples are given for hydrazide inhibition of MAO and for receptor binding of β‐carbolines. These examples reveal the power of this approach to QSAR using atom level indexes, computed directly from molecule connection tables, in which it is possible to identify atoms and regions in the molecule which are important for activity.