Comparative QSTR study of a series of alcohol derivatives against <i>Tetrahymena pyriformis</i>

Singh, Rajesh; Khan, Ahmad Khalid; Sahu, Vishnu Kumar; Singh, Priyanka

doi:10.1002/qua.21790

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…Sophisticated statistical methods, such as neural and fuzzy-neural networks, are also increasingly being applied to correlating toxicity directly to a diverse set of molecular descriptors and facilitating effective modeling of toxicity . Numerous other useful QSAR models have been developed for compounds grouped mostly by functional properties, such as substituted benzenes, carboxylic acids,and alcohols, among others. In addition, extensive computerized databases of QSAR data are available that can be used both as lateral validation of existing QSARs and also to derive new QSAR models for the derivation of heuristic rules that describe toxicity in terms of physicochemical descriptors.…”

Section: Quantitative Structure−activity (And Toxicity) Relationshipsmentioning

confidence: 99%

Toward a Comprehensive Molecular Design Framework for Reduced Hazard

2010

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Section: Quantitative Structure−activity (And Toxicity) Relationshipsmentioning

confidence: 99%

Toward a Comprehensive Molecular Design Framework for Reduced Hazard

2010

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“…This boost us to draw mathematical models for rescaling anti-leishmanial activities of carboxylates and their antimony(III) complexes. The quantum chemical parameters used in this study were well described in our previous publications [11][12][13]. EA is the electron affinity in eV, IP is the ionization potential in eV, η is the absolute hardness, and S is the global softness, µ is the dipole moment in D, ET is the total energy in hartree, ω is the electrophilicity index, and χ is the electronegativity of compounds as evaluated by DFT method…”

Section: Introductionmentioning

confidence: 99%

DFT-Based Prediction of Anti-Leishmanial Activity of Carboxylates and Their Antimony(III) Complexes Against Five Leishmanial Strains

Sahu¹,

Chandra²,

Singh³

2022

OJMR

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Carboxylates and their antimony(III) complexes experimentally scanned earlier for antileishmanial activity (IC50) against five leishmanial strains viz., L. major, L. major (Pak), L. tropica, L. mex mex, and L. donovani. These activities have been theoretically predicted by DFT method along with quantitative structure-activity relationship (QSAR) study. Molecular modeling and geometry optimization of the all the eight compounds have been performed on workspace program of CAChe Pro software of Fujitsu by opting B88-PW91 (Becke '88; Perdew & Wang '91) GGA (generalizedgradient approximation) energy functional with DZVP (double-zeta valence polarized ) basis set in DFT (Density Functional Theory). For QSAR, multiple linear regression (MLR) analysis has been performed on Project Leader Program associated with CAChe. The reliability of correlation between experimental activities and predicted activities are r 2 = 0.826, r 2 CV = 0.426 (L. major); r 2 = 0.905, r 2 CV = 0.507 (L. major (Pak)); r 2 = 0.980, r 2 CV = 0.932 (L. tropica); r 2 = 0.781, r 2 CV = 0.580 (L. mex mex) and r 2 = 0.634, r 2 CV = 0.376 (L. donovani), and a comparison of the experimental values and the values obtained by theoretical calculations has been presented pictorially that shows close resemblance.

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“…QSAR techniques increase the probability of success and reduce the time and cost involved in drug discovery, and in exploring the toxicological and ecotoxicological characteristics of molecules [11,12].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Bioconcentration Factor of Organic Compounds in Fish

Sahu¹,

Singh²

2009

CLEAN Soil Air Water

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Quantitative structure-activity relationship (QSAR) models for bioconcentration factor (BCF) prediction applicable to 131 organic compounds of different chemical structures were prepared. The study showed that the best QSAR model, p BCF = 0.00250227 M W -0.0723952 E T -0.21352 eHOMO -0.892481 eLUMO -2.58291, was made from four quantum chemical descriptors. The best model was selected on the basis of the value of the correlation coefficient (r 2 = 0.871), cross validation coefficient (r 2 CV = 0.856), standard error (Std. Err. = 0.978), standard error of estimation (Std. Err. Est. = 0.556), F statistic (F = 213.22) and p value (p = 0.009), that were calculated by Statistica software. The molecular weight followed a direct relationship with the observed bioconcentration factor ( o BCF) up to a M W of 361 Da, and thereafter it followed an inverse relationship. The total energy and HOMO-LUMO gap, showed inverse relationships with o BCF. With the help of the QSAR model, bioconcentration factors of several hypothetical molecules can be predicted prior to their synthesis.

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Comparative QSTR study of a series of alcohol derivatives against Tetrahymena pyriformis

Cited by 7 publications

References 20 publications

Toward a Comprehensive Molecular Design Framework for Reduced Hazard

Toward a Comprehensive Molecular Design Framework for Reduced Hazard

DFT-Based Prediction of Anti-Leishmanial Activity of Carboxylates and Their Antimony(III) Complexes Against Five Leishmanial Strains

Prediction of the Bioconcentration Factor of Organic Compounds in Fish

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