scholarly journals QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP ANALYSIS (QSAR) OF VINCADIFFORMINE ANALOGUES AS THE ANTIPLASMODIAL COMPOUNDS OF THE CHLOROQUINOSENSIBLE STRAIN

2010 ◽  
Vol 5 (3) ◽  
pp. 255-260
Author(s):  
Iqmal Tahir ◽  
Mudasir Mudasir ◽  
Irza Yulistia ◽  
Mustofa Mustofa
Keyword(s):  
Molecular Orbital ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Activity Data ◽  
Qsar Analysis ◽  
Relationship Analysis ◽  
Structure Activity

Quantitative Structure-Activity Relationship (QSAR) analysis of vincadifformine analogs as an antimalarial drug has been conducted using atomic net charges (q), moment dipole (), LUMO (Lowest Unoccupied Molecular Orbital) and HOMO (Highest Occupied Molecular Orbital) energies, molecular mass (m) as well as surface area (A) as the predictors to their activity. Data of predictors are obtained from computational chemistry method using semi-empirical molecular orbital AM1 calculation. Antimalarial activities were taken as the activity of the drugs against chloroquine-sensitive Plasmodium falciparum (Nigerian Cell) strain and were presented as the value of ln(1/IC50) where IC50 is an effective concentration inhibiting 50% of the parasite growth. The best QSAR model has been determined by multiple linier regression analysis giving QSAR equation: Log (1/IC50) = 9.602.qC1 -17.012.qC2 +6.084.qC3 -19.758.qC5 -6.517.qC6 +2.746.qC7 -6.795.qN +6.59.qC8 -0.190. -0.974.ELUMO +0.515.EHOMO -0.274. +0.029.A -1.673 (n = 16; r = 0.995; SD = 0.099; F = 2.682)   Keywords: QSAR analysis, antimalaria, vincadifformine.  

Study on Anti-Tumor Activity of Novel 3-Substituted 4 Anilino-Coumarin Derivatives Using Quantitative Structure-Activity Relationship (QSAR)

2019 ◽  
Vol 948 ◽  
pp. 101-108 ◽  
Author(s):  
Daratu E.K. Putri ◽  
Harno Dwi Pranowo ◽  
Winarto Haryadi
Keyword(s):  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Linear Regression Method ◽  
Coumarin Derivatives ◽  
Quantitative Structure ◽  
Activity Data ◽  
Qsar Analysis ◽  
Structure Activity ◽  
Anti Tumor Activity

Study on anti breast cancer activity of 3-substituted 4-anilino coumarin derivatives by using quantitative structure-activity relationship (QSAR) has been performed. The structures and the activity data were literatured from Guoshun et al. experiment. The molecular and electronic molecule properties were obtained from DFT/BPV86 6-31G method calculation after was through methods validation. The QSAR analysis were shown by Multi Linear Regression method (MLR). The best model of obtained for 3-substituted 4-anilino coumarin derivatives is: Log IC50 = 5.905 + (0.936 x qC1) + (-8.225 x qC8) + (-0.582 x qC13) + (11.273 x qC15) + (0.869 x ∆E) ; n = 26; r2= 0.704; Fcal/Ftab = 2.462; SEE = 0.184.

scholarly journals Quantitative structure-activity relationship to elucidate human CYP2A6 inhibition by organosulfur compounds

ADMET & DMPK ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 196-209
Author(s):  
Daniela Andrea Ramirez ◽  
Eduardo Marchevsky ◽  
Juan Maria Luco ◽  
Alejandra Camargo
Keyword(s):  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
The Body ◽  
Activity Relationship ◽  
Organosulfur Compounds ◽  
Quantitative Structure ◽  
Activity Data ◽  
Qsar Analysis ◽  
Future Design ◽  
Structure Activity

CYP2A6 is a human enzyme responsible for the metabolic elimination of nicotine, and it is also involved in the activation of procarcinogenic nitrosamines, especially those present in tobacco smoke. Several investigations have reported that reducing this enzyme activity may contribute to anti-smoking therapy as well as reducing the risk of promutagens in the body. For these reasons, several authors investigate selective inhibitors molecules toward this enzyme. The aim of this study was to evaluate the interactions between a set of organosulfur compounds and the CYP2A6 enzyme by a quantitative structure-activity relationship (QSAR) analysis. The present work provides a better understanding of the mechanisms involved, with the final goal of providing information for the future design of CYP2A6 inhibitors based on dietary compounds. The reported activity data were modeled by means of multiple regression analysis (MLR) and partial least-squares (PLS) techniques. The results indicate that hydrophobic and steric factors govern the union, while electronic factors are strongly involved in the case of monosulfides.

scholarly journals NOVEL DESIGN OF CALANONE DERIVATIVES AS ANTI-LEUKEMIA COMPOUNDS BASED ON QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP ANALYSIS

2011 ◽  
Vol 11 (1) ◽  
pp. 31-36 ◽  
Author(s):  
Ponco Iswanto ◽  
Mochammad Chasani ◽  
Harjono Harjono ◽  
Iqmal Tahir ◽  
Muhammad Hanafi ◽  
...  
Keyword(s):  
Leukemia Cell ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Pcr Analysis ◽  
Log P ◽  
Quantitative Structure ◽  
Activity Data ◽  
Relationship Analysis ◽  
Structure Activity

Leukemia drug discovery based on calanone compound was conducted in previous research and produced 6 calanone derivatives. Most of them have lower activities against leukemia cell L1210 than pure calanone. A Quantitative Structure-Activity Relationship (QSAR) analysis is conducted in this work to find more active calanone derivatives. Six compounds were used as the material of the research because they already have anti-leukemia activity data expressed in Inhibitory Concentration of Fifty Percent Cell Lethal (IC50, in mg/mL). Calculation of predictors was performed by AM1 semiempirical method. QSAR equation is determined using Principle Component Regression (PCR) analysis, with Log IC50 as dependent variable. Independent variables (predictors) are atomic net charges, dipole moment (m), and coefficient partition of n-octanol/water (Log P). This work recommends 3 novel designs of calanone derivatives that may have higher activities (in mg/mL) than those already available, i.e. gemdiol calanone (57.78), 2,4-dinitrophenylhydrazone calanone (30.94) and 2,4,6-trinitrophenylhydrazone calanone (18.96).

Quantitative Structure-Activity Relationship of Morita-Baylis-Hillman Adducts with Leishmanicidal Activity

2011 ◽  
Vol 66 (3-4) ◽  
pp. 136-142
Author(s):  
Rodrigo Octavio M. A. de Souza ◽  
José C. Barros ◽  
Joaquim F. M. da Silva ◽  
Octavio A. C. Antunes
Keyword(s):  
Molecular Orbital ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Orbital Energy ◽  
Quantitative Structure ◽  
Leishmanicidal Activity ◽  
Structure Activity ◽  
Relationship Model ◽  
Relationship Of

A quantitative structure-activity relationship model for Morita-Baylis-Hillman adducts with leishmanicidal activities was developed which correlates molecular orbital energy and dipole with percentage in the promastigote stage

scholarly journals Development of a new quantitative structure–activity relationship model for predicting Ames mutagenicity of food flavor chemicals using StarDrop™ auto-Modeller™

2021 ◽  
Vol 43 (1) ◽  
Author(s):  
Toshio Kasamatsu ◽  
Airi Kitazawa ◽  
Sumie Tajima ◽  
Masahiro Kaneko ◽  
Kei-ichi Sugiyama ◽  
...  
Keyword(s):  
Molecular Weight ◽  
Ames Test ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Low Molecular Weight ◽  
Quantitative Structure ◽  
Industrial Chemicals ◽  
Structure Activity

Abstract Background Food flavors are relatively low molecular weight chemicals with unique odor-related functional groups that may also be associated with mutagenicity. These chemicals are often difficult to test for mutagenicity by the Ames test because of their low production and peculiar odor. Therefore, application of the quantitative structure–activity relationship (QSAR) approach is being considered. We used the StarDrop™ Auto-Modeller™ to develop a new QSAR model. Results In the first step, we developed a new robust Ames database of 406 food flavor chemicals consisting of existing Ames flavor chemical data and newly acquired Ames test data. Ames results for some existing flavor chemicals have been revised by expert reviews. We also collected 428 Ames test datasets for industrial chemicals from other databases that are structurally similar to flavor chemicals. A total of 834 chemicals’ Ames test datasets were used to develop the new QSAR models. We repeated the development and verification of prototypes by selecting appropriate modeling methods and descriptors and developed a local QSAR model. A new QSAR model “StarDrop NIHS 834_67” showed excellent performance (sensitivity: 79.5%, specificity: 96.4%, accuracy: 94.6%) for predicting Ames mutagenicity of 406 food flavors and was better than other commercial QSAR tools. Conclusions A local QSAR model, StarDrop NIHS 834_67, was customized to predict the Ames mutagenicity of food flavor chemicals and other low molecular weight chemicals. The model can be used to assess the mutagenicity of food flavors without actual testing.

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