Vijay K. Agrawal

A novel PI index and its applications to QSPR/QSAR studies.

A novel topological index, PI (Padmakar-Ivan index), is derived in this paper. The index is very simple to calculate and has disseminating power similar to that of the Wiener (W) and the Szeged (Sz) indices. The comprehensive studies show that the proposed PI index correlates highly with W and Sz as well as with physicochemical properties and biological activities of a large number of diversified and complex compounds. The proposed PI index promises to be a useful parameter in the QSPR/QSAR studies. The stability of each model is demonstrated by applying cross-validation test. Furthermore, more favorable comparison with other representative indices such as the Randic index is also made in order to establish the predictive ability of the PI index. The results have shown that in several cases the PI index gave better results.

QSAR prediction of toxicity of nitrobenzenes

A QSAR analysis has been carried out on the toxicities of 40 mono-substituted nitrobenzenes using recently introduced PI and Sz indices, as well as older molecular redundancy (MRI) and Balaban indices (J). The results have shown that no statistically significant mono-parametric QSAR models are possible. Also, that along with PI, Sz, MRI and J indices are the appropriate parameters to be used in developing multiparametric QSAR models. The toxicities of nitrobenzenes are well predicted by a penta-parametric model consisting of PI, Sz, J, MRI and Ip(1) (an indicator parameter taking care of the effect of substitution at 2-position) as the correlating parameters. The predictive ability of the model is determined by a cross-validation method.

QSAR Studies on some antimalarial sulfonamides

Antimalarial activity of a series of sulfonamide derivatives (2,4-diamino-6-quinazoline sulfonamides) was modeled topologically using Wiener (W)-, and Szeged (Sz)-indices. The regression analysis of the data has shown that better results are obtained in multiparametric regressions upon introduction of indicator parameters. Predicting ability of the models was tested by r(2)(cv) values. It was observed that models based on W index gave slightly better results than those in which Sz is involved.

Prediction of lipophilicity of polyacenes using quantitative structure-activity relationships.

Predictive models for the lipophilicity (logP) of first 25 derivatives of polyacenes are reported. The models are derived from distance-based numerical descriptors which encode information about topology of each compounds in the data set. A new PI-type index called Sadhna index and abbreviated as Sd is introduced for the first time, and its relative correlation potential is established using the results obtained from Wiener (W), Szeged (Sz), first-order Randic connectivity (chi), and Padmakar-Ivan indices. The data show that lipophilicity (logP) is best modelled in bi-parametric model containing PI and Sd indices. The effect due to size, shape, branching, steric and polarity effects on the exhibition of lipophilicity is critically discussed. The predictive ability of the models is discussed on the basis of cross-validation parameters.

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.

Szeged index - Applications for drug modeling

In this review we describe various applications of Szeged (Sz) index for modeling physicochemical properties as well as physiological activities of organic compounds acting as drugs or possess pharmacological activity.

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.

Modelling of carbonic anhydrase inhibitory activity of sulfonamides using molecular negentropy.

The present paper deals with the modelling of carbonic anhydrase inhibitory activity of sulfonamides using molecular negentropy (N). Excellent results are obtained in multiple regression analysis upon introduction of indicator parameters. The results are critically discussed on the basis of statistical data obtained from regression analysis.

QSAR Studies on Carbonic Anhydrase Inhibitors: A Case of Ureido and Thioureido Derivatives of Aromatic/Heterocyclic Sulfonamides

QSAR studies on modelling of biological activity (hCAI) for a series of ureido and thioureido derivatives of aromatic/heterocyclic sulfonamides have been made using a pool of topological indices. Regression analysis of the data showed that excellent results were obtained in multiparametric correlations upon introduction of indicator parameters. The predictive abilities of the models are discussed using cross-validation parameters.

Topological approach to quantifying molecular lipophilicity of heterogeneous set of organic compounds.

The lipophilicity of the large set of organic compounds is investigated using distance-based topological indices. The results have shown that molecular lipophilicity can be modeled in multi-parametric model in that W, 1 chi, B, J and logRB along with indicator parameters are involved. The results are discussed critically.