Prediction of (13)C NMR Chemical Shift Sum Using Topological Indices: Role of Recently Introduced Balaban F and G Indices.

Present paper deals with the examination of Balaban F and G indices for estimating (13)C NMR chemical shift sums of alkanes. Set of 66 alkanes were used for this purpose, which have been divided into training set (50 compounds) and test set (16 compounds). The results have shown that Balaban G Index along with connectivity indices yields the best model. The model is analyzed for the defect due to colinearity using Ridge parameters. The most appropriate model is a three-parametric model found containing [Formula: see text], [Formula: see text], and G as correlating parameters. The results have been critically examined based on variety of statistical parameters.

QSPR Modeling of Bioconcentration Factors of Nonionic Organic Compounds.

The terms bioaccumulation and bioconcentration refer to the uptake and build-up of chemicals that can occur in living organisms. Experimental measurement of bioconcentration is time-consuming and expensive, and is not feasible for a large number of chemicals of potential regulatory concern. A highly effective tool depending on a quantitative structure-property relationship (QSPR) can be utilized to describe the tendency of chemical concentration organisms represented by, the important ecotoxicological parameter, the logarithm of Bio Concentration Factor (log BCF) with molecular descriptors for a large set of non-ionic organic compounds. QSPR models were developed using multiple linear regression, partial least squares and neural networks analyses. Linear and non-linear QSPR models to predict log BCF of the compounds developed for the relevant descriptors. The results obtained offer good regression models having good prediction ability. The descriptors used in these models depend on the volume, connectivity, molar refractivity, surface tension and the presence of atoms accepting H-bonds.

Prediction of intrinsic solubility of generic drugs using MLR, ANN and SVM analyses.

The machine learning methods artificial neural network (ANN) and support vector machine (SVM) techniques were used to model intrinsic solubility of 74 generic drugs. The models obtained were compared with those obtained using multiple linear regression (MLR) analysis. Cluster analysis was used to split the data into a training set and test set. The appropriate descriptors were selected using a wrapper approach with multiple linear regressions as target learning algorithm. The descriptor selection and model building were performed with 10 fold cross validation using the training data set. The linear model fits the training set (n = 60) with R(2) = 0.814, while ANN and SVM higher values of R(2) = 0.823 and 0.835, respectively. Though the SVM model shows improvement of training set fitting, the ANN model was slightly superior to SVM and MLR in predicting the test set. The quantitative structure-property relationship study suggests that the theoretically calculated descriptors log P, first-order valence connectivity index ((1)chi(v)), delta chi (Delta(2)chi) and information content ((2)IC) have relevant relationships with intrinsic solubility of generic drugs studied.

QSPR study on the estimation of solubility of drug-like organic compounds: a case of barbiturates.

The paper describes a method for the estimation of solubility (log S) of a series of 45 barbiturates employing 26 molecular descriptors. The molecular descriptors used being distance-based topological indices, information indices, valence connectivity index, shape indices, first-order Randic index. In addition, an indicator parameter was also used. The regression analysis has shown that an R(2) value of 0.885 was obtained in multi-parametric regression analysis. The results are discussed critically using a variety of statistical parameters. The predictive powers of the models were discussed by using the method of cross-validation. We observed that results obtained using SPSS and NCSS software are identical.

QSAR studies for the inhibition of the transmembrane carbonic anhydrase isozyme XIV with sulfonamides using PRECLAV software.

QSAR studies for the inhibition of isozyme XIV of human carbonic anhydrase (CA, EC 4.2.1.1) by a series of sulfonamides including clinically used derivatives (acetazolamide, methazolamide, ethoxzolamide, dichlorophenamide, dorzolamide, brinzolamide, benzolamide, and zonisamide) are presented. Statistical calculations done using the PRECLAV program, for the correlation between the observed inhibition values and the calculated ones were good (s = 0.2416, r(2) = 0.9259, F = 75.0196, [image omitted] ). The obtained results by using PRECLAV descriptors have been compared with those where the descriptors have been calculated with HYPERCHEM. The obtained QSAR equations pointed out the fact that the CA inhibitory activity decreased for unsubstituted (at the organic scaffold) aromatic/heteroaromatic sulfonamides, but was favored by the presence of alkyl groups substituting the scaffold, which led to a higher internal topological diversity, as well as by the presence of condensed aromatic rings in the structure of these enzyme inhibitors.

Mutagenicity of nitrated polycyclic aromatic hydrocarbons: a QSAR investigation.

Quantitative structure-activity relationship studies were performed to describe and predict the mutagenic activity of a set of 48 nitrated polycyclic aromatic hydrocarbons. From a larger pool of molecular descriptors (topological indices) we arrived at much a smaller set consisting of three correlating parameters. Such a variable selection is made using ncss software in that successive regressions were attempted using maximum-R(2) method. The results are critically discussed using a variety of statistical parameters. Our results have shown that connectivity and shape type indices together with the distance-based Wiener index (W) play a dominating role in modelling of mutagenicity (logTA100). The predictive ability of the models is discussed on the basis of cross-validated parameters.

Estimation of human carbonic anhydrase II inhibition using topological indices and their combination with quantum-theoretical descriptors.

Mathematical models were developed for the estimation of human carbonic anhydrase (CA) II inhibition. A large set of 95 CA inhibitors incorporating diverse aromatic rings were used for this purpose. The numerical descriptors used were distance- and connectivity- based indices, quantum -theoretical descriptors and Balaban and Balaban type descriptors of molecular structure. After descriptor generation, multiple linear regression analysis was performed to find superior models for estimation. The obtained results indicate that: (i) models based on topological indices are superior to those based on quantum -theoretical descriptors; (ii) combinations of topological and quantum-theoretical descriptors improves the quality of regression; (iii) in both cases involvement of Balaban and Balaban type indices is beneficial. The results are described critically based on variety of statistical parameters.

Comparative QSAR study on para-substituted aromatic sulphonamides as CAII inhibitors: information versus topological (distance-based and connectivity) indices.

Comparative quantitative structure-activity relationship studies on para-substituted aromatic sulphonamides carbonic anhydrase II (CAII) inhibitors are reported in this paper. The study is made utilizing (i) information indices along; (ii) distance-based and connectivity indices and (iii) combination of information, distance-based and connectivity type topological indices. The study has shown that distance-based and connectivity type indices are superior for modelling, monitoring and estimating CAII inhibition. The results are critically discussed using a variety of statistical parameters. Our results show that starting from the mono-parametric regression itself, our results are superior: Furthermore, our methodology allowed carrying out much higher-parametric regressions, yielding a nine-parametric model with R2 as high as 0.8375. The eight-parametric regression, gave R2= 0.8343. As there is not much difference, we have considered the eight-parametric regression the best.

QSAR studies on the activation of the human carbonic anhydrase cytosolic isoforms I and II and secretory isozyme VI with amino acids and amines.

The first QSAR study on the activation of the human secretory isoform of the metalloenzyme carbonic anhydrase (CA, EC 4.2.1.1), CA VI, with a series of amines and amino acids is reported. A large set of topological indices have been used to obtain several tri-/tetra-parametric models. We compared the CA VI activating QSAR models with those calculated for activation of the cytosolic human isozymes hCA I and hCA II. In addition, the effect of D- and L-amino acids as activators of hCA I, hCA II and of hCA VI as compared to those of structurally related biogenic amines was investigated for obtaining statistically significant and predictive QSAR equations. The obtained models are discussed using a variety of statistical parameters. The best models were obtained for hCA II activation, followed by hCA I, whereas the QSAR models for the activation of hCA VI were statistically weaker.

QSAR study on topically acting sulfonamides incorporating GABA moieties: a molecular connectivity approach.

A quantitative Structure-activity relationship study (QSAR) on a set of carbonic anhydrase (CA, EC 4.2.1.1) inhibitors is reported using first-order valence connectivity index ((1)chi(v)). The inhibitory activity against three isozymes CAI, CAII (cystolic forms), and CAIV (membrane bound form), some of which are involved in important physiological processes, were considered for this purpose. All the three activities were excellently modeled by (1)chi(v) in multi-parametric regression containing indicator parameters. The results are critically discussed on the basis of various regression parameters.

QSAR studies on benzopyran potassium channel activators.

QSAR studies on a series of benzopyrans as potassium channel activators have been carried out using a large set of distance-based topological indices. In addition, the molecular descriptors namely: negentropy and molecular redundancy indices have also been used. The relaxant potency in rat trachea, expressed as pEC(50) was used for biological characterization of the benzopyrans. The results have shown that pEC(50) can be modeled excellently in multiparametric model in that we have to include an indicator parameter. The predictive powers of the proposed models are discussed on the bases of cross-validation parameters.

Study on supramolecular complexing ability vis-à-vis estimation of pKa of substituted sulfonamides: dominating role of Balaban index (J).

The supramolecular complexing ability vis-à-vis pKa estimation of a large series of 43 sulfonamides was made using a series of molecular descriptors including topological indices. The set of topological indices chosen also contains Balaban (J) and a variety of Balaban type indices: J, Jz, Jm, Jv, Jc, and Jp. The results have shown that the most discriminating Balaban index (J) in multi-parametric regression analysis combined with indicator parameters yields excellent models and also establishes the superiority of the J index over other Balaban type indices. The statistics is improved when one of the indicator parameters is replaced by molar volume (MV). The results are discussed critically using a variety of statistics.

QSAR study on carbonic anhydrase inhibitors: water-soluble sulfonamides incorporating beta-alanyl moieties, possessing long lasting-intra ocular pressure lowering properties--a molecular connectivity approach.

A QSAR study on a series of carbonic anhydrase (CA, EC 4.2.1.1) inhibitors, and more precisely on water-soluble sulfonamides incorporating beta-alanyl moieties, possessing long lasting intra-ocular pressure lowering properties, was carried out using a series of distance-based topological indices. The regression analysis has shown that out of the pool of topological indices used, the 1chi (first-order Randic connectivity index) is the best one for modeling CA inhibitory properties against all three investigated isozymes, the cytosolic CA I, CA II and the membrane-bound CA IV, and that excellent results are obtained in multiparametric regressions. The results are critically discussed on the basis of statistical parameters.

QSAR studies on benzene sulfonamide carbonic anhydrase inhibitors: need of hydrophobic parameter for topological modeling of binding constants of sulfonamides to human CA-II.

The binding constants (logK) of benzene sulfonamides to human CA-II have been modeled using a large series of distance-based topological indices. The need or otherwise of the hydrophobic parameter (logP) for such topological modeling of logK has been examined critically. In both the cases excellent results have been obtained. In multiparametric models involving indicator parameters we observed that introduction of hydrophobic parameter (logP) yields much improved statistics. The results are discussed on the basis of statistical parameters and also by using cross-validation method.

Novel use of chemical shift in NMR as molecular descriptor: a first report on modeling carbonic anhydrase inhibitory activity and related parameters.

A novel use of NMR chemical shift of the SO(2)NH(2) protons (in dioxane as solvent) as a molecular descriptor is described for modeling the inhibition constant for benzene sulfonamides against the zinc enzyme carbonic anhydrase (CA, EC 4.2.1.1). The methodology is extended to model diuretic activity and lipophilicity of benzene sulfonamide derivatives. The regression analysis of the data has shown that the NMR chemical shift is incapable of modeling lipophilicity. However, it is quite useful for modeling the diuretic activity of these derivatives. The results are compared with those obtained using distance-based topological indices: Wiener (W)-, Szeged (Sz)-, and PI (Padmakar-Ivan) indices.

A novel method of estimation of lipophilicity using distance-based topological indices: dominating role of equalized electronegativity.

Attempt is made to propose yet another method of estimating lipophilicity of a heterogeneous set of 223 compounds. The method is based on the use of equalized electronegativity along with topological indices. It was observed that excellent results are obtained in multiparametric regression upon introduction of indicator parameters. The results are discussed critically on the basis various statistical parameters.

QSAR study on the antibacterial activity of some sulfa drugs: building blockers of Mannich bases.

Sulfa drugs are building blockers of several types of Mannich bases. Consequently, the antibacterial activities of sulfa drugs are reported in this paper, which will help in explaining and understanding antibacterial activities of Mannich bases. Reported QSAR is carried out using distance-based topological indices and discussed critically on the basis of statistical parameters.

Novel estimation of lipophilicity using 13C NMR chemical shifts as molecular descriptor.

This paper describes the use of (13)C NMR chemical shift as molecular descriptor (molecular parameter) for modeling lipophilicity (logP). A set of 32 alcohols were chosen for this purpose. The regression analysis of the data showed that (13)C NMR chemical shifts of these alcohols can be used as a molecular descriptor (molecular property) for modeling the lipophilicity (logP). Better results are obtained by introducing an indicator parameter.

QSAR study on pK(a) vis-à-vis physiological activity of sulfonamides: a dominating role of surface tension (inverse steric parameter).

The paper describes the dominating role of surface tension (ST) on the modeling, monitoring, and estimating pK(a) for a large series of 43 substituted sulfonamides. Because of the direct correlation of ST with parachor (Pc) vis-a-vis molecular volume (MV), ST is considered as a steric parameter. Single as well as multi-parametric regressions have indicated that ST has a dominating role in QSAR of the set of sulfonamides used and that excellent results are obtained in multi-parametric regression analysis. The results are discussed critically on the basis of statistical parameters.

QSAR study on phosphoramidothioate (Ace) toxicities in housefly.

The present paper describes quantitative structure-activity relationships (QSARs) formulated for a series of phosphoramidothioate (Ace II) analogs. The k(e) values for Ace II-induced inhibition of fly-acetylcholinesterase as well as LD50 for housefly exposed to Ace II analogs were governed by distance-based topological as well as information theoretic indices. In addition, we have also modeled lipophilicity of the phosphoramidothioates used. Excellent results are obtained in each case. The predictive ability of the models were discussed on the basis of cross-validated parameters.