Molecular Connectivity Indices Research Articles

Prediction of critical micelle concentration of cationic surfactants using connectivity indices

Relationship for predicting Logcmc for cationic surfactants having chloride as counterion from only molecular connectivity indices was found. It is suggested that the index 0χν includes some information about hydrophobicity while indices 4χ pc and $${^{4}\chi^{\nu}_{pc}}$$ include some information about hydrophilicity of the cationic surfactants studied. The structures of 23 compounds used for the correlation are quite diverse.

4-[1-(Substituted aryl/alkyl carbonyl)-benzoimidazol-2-yl]-benzenesulfonic acids: Synthesis, antimicrobial activity, QSAR studies, and antiviral evaluation

A series of 4-[1-(substituted aryl/alkyl carbonyl)-benzoimidazol-2-yl]-benzene sulphonic acids ( 1-20) was synthesized and evaluated, in vitro, for their antimicrobial activity and the results indicated that compounds 4-[1-(4-Nitrobenzoyl)-1H-benzoimidazol-2-yl]-benzenesulfonic acid ( 9) and 4-(1-octadec-9-enoyl-1H-benzoimidazol-2-yl)-benzenesulfonic acid ( 18) were found to be the most active ones. QSAR investigations indicated that the multi-target QSAR model was effective in describing the antimicrobial activity over the one-target QSAR models. Further the mt-QSAR model indicated the importance of the topological parameter, Balaban index (J) followed by the electronic parameter, LUMO and topological parameter, valence second order molecular connectivity index ( 2χ v) in describing the antimicrobial activity of synthesized compounds ( 1-20).

2‐(pyrazin‐2‐yloxy)acetohydrazide Analogs QSAR Study: An Insight into the Structural Basis of Antimycobacterial Activity

Quantitative structure activity relationship analysis based on classical Hansch approach was adopted on reported novel series of 2-(pyrazin-2-yloxy)acetohydrazide analogs. Various types of descriptors like topological, spatial, thermodynamic, and electronic were used to derive a quantitative relationship between the antitubercular activity and structural properties. The consensus scoring function showed a significant statistics of training and test set. Coefficient of determination (r²) of consensus model and predictive squared correlation coefficient (r²(pred)) were found to be 0.889 and 0.782, respectively. The model is not only able to predict the activity of test compounds but also explained the important structural features of the molecules in a quantitative manner. The study revealed that antimycobacterium activity is predominantly explained by the molecular connectivity indices of length 6, hydrogen donor feature of the analogs, and shape factors of the substituent. The comparative investigation of antibacterial activity against Staphylococcus aureus, Bacillus subtilis, Escherichia coli, and Salmonella typhi provided structural insights on how modulation of the molecular connectivity indices, energy of lowest unoccupied molecular orbital, accessible surface area, and moment of inertia of the analogs could be usefully made to optimize the antibacterial activity.

Estimation of standard enthalpy of formation of alkanes in gaseous state by calculating size, structural and electronic parameters in the molecules

A quantitative analysis is made on the correlation ship of thermodynamic property, i.e., standard enthalpy of formation (ΔH f0) with Kier’s molecular connectivity index(1Xv),vander waal’s volume (Vw) electrotopological state index (E) and refractotopological state index (R) in gaseous state of alkanes. The regression analysis reveals a significant linear correlation of standard enthalpy of formation (ΔH f0) with 1Xv, Vw, E and R. The equations obtained by regression analysis may be used to estimate standard enthalpy of formation (ΔH f0) of alkanes in gaseous state.

QSRR Models for Kováts’ Retention Indices of a Variety of Volatile Organic Compounds on Polar and Apolar GC Stationary Phases Using Molecular Connectivity Indexes

Quantitative structure-retention relationship (QSRR) approaches, based on molecular connectivity indices are useful to predict the gas chromatography of Kováts relative retention indices (GC-RRIs) of 132 volatile organic compounds (VOCs) on different 12 (4 apolar and 8 polar) stationary phases (C67, C103, C78, C∞, POH, TTF, MTF, PCL, PBR, TMO, PSH and PCN) at 130 °C. Full geometry optimization based on Austin model 1 semi-empirical molecular orbital method was carried out. The sets of 30 molecular descriptors were derived directly from the topological structures of the compounds from DRAGON program. By means of the final variable selection method, which is elimination selection stepwise regression algorithms, three optimal descriptors were selected to develop a QSRR model to predict the RRI of organic compounds on each stationary phase with a correlation coefficient between 0.9378 and 0.9673 and a leave-one-out cross-validation correlation coefficient between 0.9325 and 0.9653. The root mean squares errors over different 12 phases were within the range of 0.0333–0.0458. Furthermore, the accuracy of all developed models was confirmed using procedures of Y-randomization, external validation through an odd–even number and division of the entire dataset into training and test sets. A successful interpretation of the complex relationship between GC RRIs of VOCs and the chemical structures was achieved by QSRR. The three connectivity indexes in the models are also rationally interpreted, which indicated that all organic compounds’ RRI was precisely represented by molecular connectivity indexes.Electronic supplementary materialThe online version of this article (doi:10.1365/s10337-010-1741-4) contains supplementary material, which is available to authorized users.

Estimation of standard enthalpy of formation of alkanes in gaseous state by calculating size, structural and electronic parameters in the molecules

A quantitative analysis is made on the correlation ship of thermodynamic property, i.e., standard enthalpy of formation (ΔH fo) with Kier's molecular connectivity index(1Xv),vander waal's volume (Vw) electrotopological state index (E) and refractotopological state index (R) in gaseous state of alkanes. The regression analysis reveals a significant linear correlation of standard enthalpy of formation (ΔH fo) with 1Xv, Vw, E and R. The equations obtained by regression analysis may be used to estimate standard enthalpy of formation (ΔH fo) of alkanes in gaseous state.

Determination of the soil–water partition coefficients (log KOC) of some mono- and poly-substituted phenols by reversed-phase thin-layer chromatography

In order to determine the soil–water partition coefficient for eleven mono- and poly-substituted phenolic compounds, for which there is still no literature data available, the possibility of using thin-layer chromatography (TLC) as a means for rapid and reliable log K OC estimation was examined. A series of chromatographically derived descriptors: R M 0 , b, C 0 and PC1 (first principal component), calculated from retention data obtained under reversed-phase conditions, were used for the assessment of models as well as for a direct calibration procedure. The final calibration models are discussed with regard to the achieved accuracy and statistical quality, the type of descriptors used and the corresponding chromatographic conditions. The estimated log K OC values of the studied phenols were compared with those obtained by other means: (a) the present OECD guideline based on an HPLC technique; (b) the KOCWIN software package, available free of charge from the US Environmental Protection Agency web site and (c) general LSER models established by Nguyen and coworkers, and Poole and coworkers. The proposed method showed the best agreement with the results obtained by the OECD procedure, followed by the LSER models of Poole and Nguyen. Lower quality correlations were achieved with the KOCWIN calculated values, especially those predicted by molecular connectivity indices.

Modelling the effect of mixture components on permeation through skin

A vehicle influences the concentration of penetrant within the membrane, affecting its diffusivity in the skin and rate of transport. Despite the huge amount of effort made for the understanding and modelling of the skin absorption of chemicals, a reliable estimation of the skin penetration potential from formulations remains a challenging objective. In this investigation, quantitative structure–activity relationship (QSAR) was employed to relate the skin permeation of compounds to the chemical properties of the mixture ingredients and the molecular structures of the penetrants. The skin permeability dataset consisted of permeability coefficients of 12 different penetrants each blended in 24 different solvent mixtures measured from finite-dose diffusion cell studies using porcine skin. Stepwise regression analysis resulted in a QSAR employing two penetrant descriptors and one solvent property. The penetrant descriptors were octanol/water partition coefficient, log P and the ninth order path molecular connectivity index, and the solvent property was the difference between boiling and melting points. The negative relationship between skin permeability coefficient and log P was attributed to the fact that most of the drugs in this particular dataset are extremely lipophilic in comparison with the compounds in the common skin permeability datasets used in QSAR. The findings show that compounds formulated in vehicles with small boiling and melting point gaps will be expected to have higher permeation through skin. The QSAR was validated internally, using a leave-many-out procedure, giving a mean absolute error of 0.396. The chemical space of the dataset was compared with that of the known skin permeability datasets and gaps were identified for future skin permeability measurements.

Dodecanoic acid derivatives: Synthesis, antimicrobial evaluation and development of one-target and multi-target QSAR models

In this study a series of dodecanoic acid derivatives (1–30) were synthesized and evaluated for in vitro antimicrobial activity against the panel of Gram positive, Gram negative bacterial and fungal strains. 4-Nitro phenyl dodecanoate (4) and quinolin-8-yl dodecanoate (5) emerged as most effective antibacterial agents, and 1-(4-benzylpiperazin-1-yl) dodecan-1-one (15) was found to be the most effective antifungal agent amongst the synthesized dodecanoic acid derivatives. Quantitative structure activity relationship (QSAR) studies performed by the development of one-target and multi-target models indicated that multi-target model was effective in describing the antimicrobial activity of dodecanoic acid derivatives as well demonstrated the importance of topological parameter, zero-order molecular connectivity index (0χ).

ChemInform Abstract: Neural Network Based Quantitative Structural Property Relations (QSPRs) for Predicting Boiling Points of Aliphatic Hydrocarbons.

Quantitative structural property relations (QSPRs) for boiling points of aliphatic hydrocarbons were derived using a back-propagation neural network and a modified Fuzzy ARTMAP architecture. With the back-propagation model, the selected molecular descriptors were capable of distinguishing between diastereomers. The QSPRs were obtained from four valance molecular connectivity indices (1χv,2χv,3χv,4χv), a second-order Kappa shape index (2κ), dipole moment, and molecular weight. The inclusion of dipole moment proved to be particularly useful for distinguishing between cis and trans isomers. A back-propagation 7−4−1 architecture predicted boiling points for the test, validation, and overall data sets of alkanes with average absolute errors of 0.37% (1.65 K), 0.42% (1.73 K), and 0.37% (1.54 K), respectively. The error for the test and overall data sets decreased to 0.19% (0.81 K) and 0.31% (1.30 K), respectively, using the modified Fuzzy ARTMAP network. A back-propagation alkene model, with a 7−10−1 architectu...

Application of BP Neural Network into the Kow of Chemical Contaminants

The octanol / water partition coefficient (Kow) is an important physical parameters to describe their behavior in the environment. However, because of some reasons, it is difficult to determine the octanol / water partition coefficient of each compound accurately. In this paper, we will introduce RBF neural network and molecular bond connectivity index to forecast the solubility of organic compounds in water. The result is better using the BP network to predict, the correlation coefficient has achieved 0.998, the prediction error in the permission scope.

Erratum to: Improved superaugmented eccentric connectivity indices for QSAR/QSPR part I: development and evaluation

Recently reported, new-generation superaugmented eccentric connectivity indices $$ \left( {^{SA} \xi^{\text{c}} } \right) $$ have been significantly improved for the purpose of QSAR/QSPR modeling. Four new superaugmented eccentric connectivity indices (denoted by: $$ ^{SA} \xi_{4}^{\text{c}} ,\,^{SA} \xi_{5}^{\text{c}} ,\,^{SA} \xi_{6}^{\text{c}} , $$ and $$ ^{SA} \xi_{7}^{\text{c}} $$ ) along with their topochemical versions (denoted by: $$ ^{SAc} \xi_{4}^{\text{c}} ,\,^{SAc} \xi_{5}^{\text{c}} ,\,^{SAc} \xi_{6}^{\text{c}} ,$$ and $$ ^{SAc} \xi_{7}^{\text{c}} $$ ) have been conceptualized in the present study. The values of these indices for all the possible structure of three, four, and five vertices were computed using an in-house computer program. Proposed topological indices revealed absence of intercorrelation with Wiener’s index (W), Balaban’s index (D), Randic’s molecular connectivity index (χ), and eccentric connectivity index (ξc). The superaugmented eccentric connectivity index-7 $$ \left( {^{SA} \xi_{7}^{\text{c}} } \right) $$ exhibited exceptionally high discriminating power of the order of >300,000 for all possible structures containing only five vertices. High sensitivity toward branching, exceptionally high discriminating power amalgamated with negligible degeneracy offer proposed indices a vast potential for use in the characterization of structures, similarity/dissimilarity studies, lead identification, lead optimization, combinatorial library design, and quantitative structure–activity/structure–property relationships to facilitate drug design.

Study on QSTR of Benzoic Acid Compounds with MCI

Quantitative structure-toxicity relationship (QSTR) plays an important role in toxicity prediction. With the modified method, the quantum chemistry parameters of 57 benzoic acid compounds were calculated with modified molecular connectivity index (MCI) using Visual Basic Program Software, and the QSTR of benzoic acid compounds in mice via oral LD50 (acute toxicity) was studied. A model was built to more accurately predict the toxicity of benzoic acid compounds in mice via oral LD50: 39 benzoic acid compounds were used as a training dataset for building the regression model and 18 others as a forecasting dataset to test the prediction ability of the model using SAS 9.0 Program Software. The model is LogLD50 = 1.2399 × 0JA +2.6911 × 1JA – 0.4445 × JB (R2 = 0.9860), where 0JA is zero order connectivity index, 1JA is the first order connectivity index and JB = 0JA × 1JA is the cross factor. The model was shown to have a good forecasting ability.

Benzylidene/2-chlorobenzylidene hydrazides: Synthesis, antimicrobial activity, QSAR studies and antiviral evaluation

A series of benzylidene hydrazides ( 1– 20) was synthesized and tested, in vitro, for antibacterial, antifungal and antiviral activities. The microbial screening results indicated that compounds having chloro and nitro substituents were the most active ones. The antiviral evaluation depicted that compounds 9 and 19 were active against Vesicular stomatitis virus (VSV) in HeLa cell cultures. QSAR investigations indicated that the multi-target QSAR model was effective in describing the antimicrobial (antibacterial and antifungal) activity over the one-target QSAR models. Further the mt-QSAR model indicated that the topological parameters, second order molecular connectivity index ( 2 χ) and third order Kier's alpha shape index ( κα 3) are effective in describing the antimicrobial activity of synthesized hydrazides.

Design of ionic liquids via computational molecular design

Computational molecular design (CMD) is a methodology which applies optimization techniques to develop novel lead compounds for a variety of applications. In this work, a CMD method is applied to the design of ionic liquids (ILs), which are being considered for use as environmentally benign solvents. The molecularly tunable nature of ILs yields an extraordinary number of possible cation and anion combinations, the majority of which have never been synthesized. The product design framework developed in this work seeks to accelerate the commonly used experimental trial-and-error approach by searching through this large molecular space and providing a set of chemical structures likely to match a set of desired property targets. To predict the physical and chemical properties of an ionic liquid in a specific system, quantitative structure–property relations (QSPRs) have been developed. In this work, correlations were created for solubility, diffusivity, and melting temperature. The electronic structure of ionic liquids is quantified using molecular connectivity indices, which describe bonding environments, charge distribution, orbital hybridization and other interactions within and between ions. The resulting property prediction model is then integrated within a computational molecular design framework, which combines the QSPRs with structural feasibility constraints in a combinatorial optimization problem. The problem is reformulated as an MILP after exact linearization of structural constraints. An example is provided to test the formulation for the design of ionic liquids for use within a hydrofluorocarbon (refrigerant) gas separation system. A second example compares a stochastic optimization algorithm, Tabu Search, to a standard deterministic solver for the solution of a larger-scale refrigeration design problem. The computational efficiency and practical implementation of this product design methodology is also discussed.

A QSAR Study on Some Series of Anti-Hepatitis C Virus (HCV) Agents

Hepatitis C virus (HCV) is a major human pathogen, infecting an estimated 170 million persons worldwide, roughly five times the number infected by HIV type 1. A substantial fraction of these HCV infected individuals develop serious progressive liver disease, including cirrhosis and hepatocellular carcinoma. The current treatment regimen suffers from various side effects. These limitations encourage the search of more effective HCV inhibitors. To achieve this objective a quantitative structure-activity relationship (QSAR) study has been made on three different series of anti-HCV agents that are comprised of pyrrolo[2,3-d]pyrimidine nucleoside derivatives, benzimidazole-coumarin conjugates, and ribonucleoside analogues, respectively. The QSAR analysis reveals that anti-HCV potencies of these series of compounds are controlled by a topological parameter (Kiers first order valence molecular connectivity index 1χv), lipophilicity, and the molar refractivity of the compounds, respectively. Based on these results, the mechanisms of drug-receptor interactions of these compounds are discussed, which may be of great help to design and develop more anti-HCV agents.

Prediction of Critical Micelle Concentration of Nonionic Surfactants by a Quantitative Structure – Property Relationship

A quantitative structure - property relationship (QSPR) was used to predict the critical micelle concentration (cmc) of nonionic surfactants. The relation was developed for a diverse set of 23 nonionic surfactants (r = 0.99, F-test = 1042.5) employing the connectivity and valence connectivity indices only. The molecular connectivity indices were calculated for the whole molecule which was a simple and general approach.

Evaluation of Combined Toxicity of Phenols and Lead to Photobacterium phosphoreum and Quantitative Structure–Activity Relationships

The combined toxicity of lead (Pb) and nine phenols were measured. The result indicated that the combined toxicity is not only dependent on the Pb concentrations but also on the positions of substituted groups of phenols. Quantitative structure-activity relationship equations were built from the combined toxicity and physico-chemical descriptors of phenols in the different Pb concentrations. The combined toxicity was related to water solubility and the third order molecular connectivity index ((3)X) in low Pb concentration, to solute excess molar refractivity (E) and ionization constant (pKa) in medium Pb concentration and to dipolarity/polarizability (S) in high Pb concentration.

Quantitative structure–activity relationship (QSAR) of artemisinin: the development of predictive in vivo antimalarial activity models

AbstractA quantitative structure–activity relationship (QSAR) analysis has been performed on a data set of 194 artemisinin analogs for antimalarial activity. Several types of descriptors including topological, spatial, thermodynamics, information content, lead likeness, and E‐state indices have been used to derive a quantitative relationship between antimalarial activity and structural properties. A systematic approach of zero tests, missing value test, simple correlation test, multicollinearity test, and genetic algorithm method of variable selection was used to generate the model. Statistically significant model (r2 = 0.845, q = 0.799, F‐test = 53.40) was obtained with the descriptors like molecular connectivity indexes, E‐state index, length‐to‐breadth ratio of compounds, MLog P, HOMO, electron density, Balabans topological index, and strain energy of the molecules. The robustness of the QSAR models was characterized by the values of the internal leave one out cross‐validated regression coefficient (q) for the training set and determination coefficient in prediction, q for the test set. The value of q = 0.876 for the test set; revealed good external predictability of the QSAR model. Also, for an external data set (validation set) of four artemisinin analogs, the QSAR model was able to predict the antimalarial activity very well in comparison to experimental values. The model was also tested successfully for external validation criteria. The QSAR model developed in this study should aid further design of novel potent artemisinin derivatives. Copyright © 2009 John Wiley & Sons, Ltd.

Graph-theoretical analysis of structure-property and structure-activity correlations

An exposition of a potentially useful graph-theoretical method for comparing structures is given. The concept of rigorous comparison of sequences, introduced into mathematics by Muirhead and generalized by Karamata, is applied to contributing terms in bond-additive schemes. Such an approach illuminates the origin for occasional deviations in the trends for structure-property and structure-activity correlations. The method is illustrated on the trends of boiling points and heats of vaporizations in hexane and heptane isomers and the corresponding trends for molecular connectivity indices of the molecules considered. The outlined concept of comparability of structures not only clarifies a posteriori occasional discrepancies in correlations but may also a priori suggest circumstances when one cannot expect simple bond additivity. With the help of the comparability tests the empirical curve-fitting procedures no longer need be based on guessing, intuition, and analogy, since necessity for additional parameters in such schemes can be assessed in advance.