Amrāvati, India
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Mahajan D.T.,Vidya Bharati College Camp | Masand V.H.,Vidya Bharati College Camp | Patil K.N.,Vidya Bharati College Camp | Hadda T.B.,University Mohammed Premier | Rastija V.,Josip Juraj Strossmayer University of Osijek
Medicinal Chemistry Research | Year: 2013

In the present study, we have carried out extensive GUSAR and conventional 3D QSAR analyses of 49 synthetic prodiginines possessing moderate to high activities against multi drug resistant strain of Plasmodium falciparum. 2D and 3D descriptors, various statistical parameters, viz. R 2, R adj 2, standard error, Y-randomization, etc., were checked to build successful QSAR model. The best four parametric GA-MLR 3D-QSAR model was found to have R train 2 = 0.84; R adj 2 = 0.83. GUSAR analysis was performed to vindicate the QSAR results and get additional results. The consensus GUSAR model based on QNA descriptor is found to have R train 2 = 0.80 and Q train 2 = 0.76. The analyses reveal that certain groups/atoms like -F, benzylic -CH2- and -OCH3 play crucial role in deciding the antimalarial activity of prodiginines. The analyses could be useful to improve the antimalarial activity of these biologically privileged molecules. © 2012 Springer Science+Business Media, LLC.

Rastija V.,Josip Juraj Strossmayer University of Osijek | Masand V.H.,Vidya Bharati College Camp
Combinatorial Chemistry and High Throughput Screening | Year: 2014

In order to find a thriving quantitative structure-activity relationship for antitrypanosomal activities (against Trypanosma brucei rhodesiense) of polyphenols that belong to different structural groups, multiple linear regression (MLR) and artificial neural networks (ANN) were employed. The analysis was performed on two different-sized training sets (59% and 78% molecules in the training set), resulting in relatively successful MLR and ANN models for the data set containing the smaller training set. The best MLR model obtained using the five descriptors (R3m+, GAP, DISPv, HATS2m, JGI2) was able to account only for 74% of the variance of antitrypanosomal activities of the training set and achieved a high internal, but low external prediction. Nonlinearities of the best ANN model compared with the linear model improved the coefficient of determination to 98.6%, and showed a better external predictive ability. The obtained models displayed relevance of the distance between oxygen atoms in molecules of polyphenols, as well as stability of molecules, measured by the difference between the energy of the highest occupied molecular orbital and the energy of the lowest unoccupied molecular orbital (GAP) for their activity. © 2014 Bentham Science Publishers.

Masand V.H.,Vidya Bharati College Camp | Toropov A.A.,Instituto Of Ricerche Farmacologiche Mario Negri | Toropova A.P.,Instituto Of Ricerche Farmacologiche Mario Negri | Mahajan D.T.,Vidya Bharati College Camp
Current Computer-Aided Drug Design | Year: 2014

In the present study, predictive quantitative structure - activity relationship (QSAR) models for anti-malarial activity of 4-aminoquinolines have been developed. CORAL, which is freely available on internet (, has been used as a tool of QSAR analysis to establish statistically robust QSAR model of anti-malarial activity of 4-aminoquinolines. Six random splits into the visible sub-system of the training and invisible subsystem of validation were examined. Statistical qualities for these splits vary, but in all these cases, statistical quality of prediction for anti-malarial activity was quite good. The optimal SMILES-based descriptor was used to derive the single descriptor based QSAR model for a data set of 112 aminoquinolones. All the splits had r2> 0.85 and r2> 0.78 for subtraining and validation sets, respectively. The three parametric multilinear regression (MLR) QSAR model has Q2 = 0.83, R2 = 0.84 and F = 190.39. The anti-malarial activity has strong correlation with presence/absence of nitrogen and oxygen at a topological distance of six. © 2014 Bentham Science Publishers.

Masand V.H.,Vidya Bharati College Camp | Mahajan D.T.,Vidya Bharati College Camp | Hadda T.B.,University Mohammed Premier | Jawarkar R.D.,P.A. College | And 3 more authors.
Medicinal Chemistry Research | Year: 2014

Indolylarylsulfones (IASs) have received considerable interest during the last decades due to high potency against HIV-1 as non-nucleoside reverse transcriptase inhibitors. In present work, quantitative structure-activity relationship (QSAR) and molecular docking analyses were performed to model the anti-HIV-1 activity of 36 IASs. 2D and 3D-descriptors, genetic algorithm, internal and external validations were used to develop statistically robust four-parametric QSAR models. The best QSAR model is with R tr 2 = 0.8608. The QSAR analysis reveals that the activity of IASs depends on the presence of electronegative and heavy atoms at the internal atmosphere of the compounds. The docking analysis reveals that lipophilic and H-bonding interactions are the prominent interactions among IASs and the receptor. The QSAR analysis proved to be a useful tool in the prediction of anti-HIV-1 activity of congeneric compounds and some important insights were also found that will be useful to guide for the synthesis of new IASs with improved activity. © 2013 Springer Science+Business Media New York.

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