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.

Prediction of human volume of distribution values for drugs using linear and nonlinear quantitative structure pharmacokinetic relationship models.

In the present study the volume of distribution values in humans of 121 drugs was estimated using quantitative structure pharmacokinetic relationship analysis. The multiple linear regression (MLR) method and nonlinear artificial neural network (ANN) and support vector machines (SVM) were employed for modeling. The theoretically calculated molecular descriptors were used for modeling and best set of descriptors selected by correlation based feature selection (CFS) method. The performance and predictive capability of linear method was investigated and compared with nonlinear method. The ANN gave better model with an average fold error of 1.66. The test set prediction accuracy shows human volume of distribution values could be predicted, on average, within 2-fold of the actual value.

Exploring QSARs of the interaction of flavonoids with GABA (A) receptor using MLR, ANN and SVM techniques.

Quantitative Structure-Activity Relationship (QSAR) models for binding affinity constants (log Ki) of 78 flavonoid ligands towards the benzodiazepine site of GABA (A) receptor complex were calculated using the machine learning methods: artificial neural network (ANN) and support vector machine (SVM) techniques. The models obtained were compared with those obtained using multiple linear regression (MLR) analysis. The descriptor selection and model building were performed with 10-fold cross-validation using the training data set. The SVM and MLR coefficient of determination values are 0.944 and 0.879, respectively, for the training set and are higher than those of ANN models. Though the SVM model shows improvement of training set fitting, the ANN model was superior to SVM and MLR in predicting the test set. Randomization test is employed to check the suitability of the models.

Estimation of Anti-HIV Activity of HEPT Analogues Using MLR, ANN, and SVM Techniques.

The present study deals with the estimation of the anti-HIV activity (log1/C) of a large set of 107 HEPT analogues using molecular descriptors which are responsible for the anti-HIV activity. The study has been undertaken by three techniques MLR, ANN, and SVM. The MLR model fits the train set with R (2)=0.856 while in ANN and SVM with higher values of R (2) = 0.850, 0.874, respectively. SVM model shows improvement to estimate the anti-HIV activity of trained data, while in test set ANN have higher R (2) value than those of MLR and SVM techniques. R m (2) = metrics and ridge regression analysis indicated that the proposed four-variable model MATS5e, RDF080u, T(O⋯O), and MATS5m as correlating descriptors is the best for estimating the anti-HIV activity (log 1/C) present set of compounds.

Quantitative structure-pharmacokinetic relationship (QSPkP) analysis of the volume of distribution values of anti-infective agents from J group of the ATC classification in humans.

In this study, a quantitative structure-pharmacokinetic relationship (QSPkR) model for the volume of distribution (Vd) values of 126 anti-infective drugs in humans was developed employing multiple linear regression (MLR), artificial neural network (ANN) and support vector regression (SVM) using theoretical molecular structural descriptors. A correlation-based feature selection (CFS) was employed to select the relevant descriptors for modeling. The model results show that the main factors governing Vd of anti-infective drugs are 3D molecular representations of atomic van der Waals volumes and Sanderson electronegativities, number of aliphatic and aromatic amino groups, number of beta-lactam rings and topological 2D shape of the molecule. Model predictivity was evaluated by external validation, using a variety of statistical tests and the SVM model demonstrated better performance compared to other models. The developed models can be used to predict the Vd values of anti-infective drugs.

SAR studies on ß-cell K(ATP) channel openers.

Recently Balaban has introduced a J-type G index for modeling those properties/activities where not only the "shape" but also size of the graph influences the property/activity of organic compounds acting as drugs. Here, for the first time G index has been used for modeling the pancreatic ß-cell K(ATP) channel openers viz R/S-3,4-dihydro-2,2-dimethyl-6-halo-4-(substituted phenylaminocarbonylamino)-2H-1-benzopyrans. Modeling technology used in this paper has established that the referred activity is excellently modeled by using G as one of the correlating descriptors. The results are critically discussed using Ridge statistics.

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.

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.

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.

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.

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 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.

QSAR study on carbonic anhydrase inhibitors: aromatic/heterocyclic sulfonamides containing 8-quinoline-sulfonyl moieties, with topical activity as antiglaucoma agents.

Quantitative structure-activity-relationship (QSAR) study on aromatic/heterocyclic sulfonamides containing 8-quinoline-sulfonyl carbonic anhydrase (CA) inhibitors has been carried out topologically using first-order valence connectivity index ((1)chi(v)). Excellent results are obtained against all the three isozymes; CA I, II and IV of the zinc enzyme CA by using indicator parameters along with (1)chi(v).

Quantitative structure-activity relationship study on sulfanilamide schiff's bases: carbonic anhydrase (CA) inhibitors.

The paper deals with quantitative structure-activity studies on a group of sulfanilamide Schiff's base inhibitors of carbonic anhydrase (CA) using distance-based topological indices. The regression analysis of the data has shown that the activities of the compounds used in inhibiting Carbonic AnhydraseII (CAII) activity can be modeled excellently in multi-parametric model in that some indicator parameters are also involved. The results are discussed critically.

QSAR study on 5-lipoxygenase inhibitors using distance-based topological indices.

QSAR study on a large set of 5-lipoxygenase inhibitors has been carried out using distance-based topological indices. Regression analysis of the data has indicated that an excellent model is obtained when these topological indices are combined with some classical molecular descriptors. The obtained models are critically discussed and examined.

QSAR Study on tadpole narcosis.

This paper deals with a Quantitative Structure-Activity Relationship (QSAR) study on a large set of 123 compounds using a combination of topological indices as well as Abraham's molecular descriptors. The results have shown that an excellent model (R=0.9542) is obtained in hexa-parametric correlation containing W, logRB (topological indices) along with R2, sigmapi2H, sigmabeta2O and Vx as the correlating parameters. The results are discussed critically.