Showing papers by "Surendra Kumar published in 2013"
TL;DR: In‐silico ADME absorption, distribution, metabolism and excretion profiling and toxicity risk assessment test was performed, and results showed that majority of compounds from current dataset and newly virtually screened hits generated were within their standard limit.
Abstract: A grid potential analysis employing a novel approach of 3D quantitative structure–activity relationships (QSAR) as AutoGPA module in MOE2009.10 was performed on a dataset of 42 compounds of N-arylsulfonylindoles as anti-HIV-1 agents. The uniqueness of AutoGPA module is that it automatically builds the 3D-QSAR model on the pharmacophore-based molecular alignment. The AutoGPA-based 3D-QSAR model obtained in the present study gave the cross-validated Q2 value of 0.588, r2pred value of 0.701, r2m statistics of 0.732 and Fisher value of 94.264. The results of 3D-QSAR analysis indicated that hydrophobic groups at R1 and R2 positions and electron releasing groups at R3 position are favourable for good activity. To find similar analogues, virtual screening on ZINC database was carried out using generated AutoGPA-based 3D-QSAR model and showed good prediction. In addition to those mentioned earlier, in-silico ADME absorption, distribution, metabolism and excretion profiling and toxicity risk assessment test was performed, and results showed that majority of compounds from current dataset and newly virtually screened hits generated were within their standard limit. Copyright © 2013 John Wiley & Sons, Ltd.
8 citations
TL;DR: The developed QSAR model shows that hydrophobicity, flexibility, three dimensional surface area, volume and shape of molecule are important parameters to be considered for designing new compounds and to decipher reverse transcriptase enzyme inhibition activity of these compounds at molecular level.
Abstract: The emergence of mutant virus in drug therapy for HIV-1 infection has steadily risen in the last decade. Inhibition of reverse transcriptase enzyme has emerged as a novel target for the treatment of HIV infection. The aim to decipher the structural features that interact with receptor, we report a quantitative structure activity relationship (QSAR) study on a dataset of thirty seven compounds belonging to bisphenylbenzimidazoles (BPBIs) as reverse transcriptase inhibitors using enhanced replacement method (ERM), stepwise multiple linear regression (Stepwise-MLR) and genetic function approximation (GFA) method for selecting a subset of relevant descriptors, developing the best multiple linear regression model and defining the QSAR model applicability domain boundaries. The enhanced replacement method was found to give better results r²=0.8542, Q²(loo) = 0.7917, r²pred = 0.7812) at five variables as compared to stepwise MLR and GFA method, evidenced by internal and external validation parameters. The modified r² (r²m) of the training set, test set and whole data set were calculated and are in agreement with the enhanced replacement method. The results of QSAR study rationalize the structural requirement for optimum binding of ligands. The developed QSAR model shows that hydrophobicity, flexibility, three dimensional surface area, volume and shape of molecule are important parameters to be considered for designing new compounds and to decipher reverse transcriptase enzyme inhibition activity of these compounds at molecular level. The applicability domain was defined to find the similar analogs with better prediction power.
5 citations