STRUCTURAL FEATURE STUDY OF FUROXAN DERIVATIVES AS POTENT ANTI-TRYPANOSOMATID AGENTS: QSAR APPROACH

INDIAN DRUGS ◽  
2016 ◽  
Vol 53 (09) ◽  
pp. 12-21
Author(s):  
M. C. Sharma ◽  
Keyword(s):  
Cross Validation ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Two Dimensional ◽  
Quantitative Structure ◽  
Property A ◽  
Qsar Studies ◽  
Structure Activity

Two-dimensional quantitative structure–activity relationship (QSAR) studies of anti-trypanosomatid, furoxan alkylnitrate derivatives have been carried out. This study aims at establishing a quantitative structure activity relationship between furoxan alkylnitrate molecule and their anti-trypanosomatid property. A statistically best QSAR model was obtained with a correlation coefficient r2 of 0.8559, cross validation coefficient, q2 of 0.8072 and pred_r2 value of 0.8217. Various 2D descriptors were calculated and used in the present analysis. The descriptors SdssS (sulfone) count and SdsNE-index suggested that sulphone and NO2 groups at the R1 and R2 positions of furoxan moiety will increases anti-trypanosomatid activity. It will be useful to build a QSAR model to correlate the properties of new untested furoxan derivatives with their anti-trypanosomatid activity.

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.

Using nano-QSAR to determine the most responsible factor(s) in gold nanoparticle exocytosis

RSC Advances ◽  
2015 ◽  
Vol 5 (70) ◽  
pp. 57030-57037 ◽  
Author(s):  
Arafeh Bigdeli ◽  
Mohammad Reza Hormozi-Nezhad ◽  
Hadi Parastar
Keyword(s):  
Gold Nanoparticles ◽  
Gold Nanoparticle ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Structure Activity ◽  
Determining Factors

A nano-quantitative structure-activity relationship (nano-QSAR) model is proposed to indicate the determining factors responsible in the exocytosis of gold nanoparticles in macrophages.

scholarly journals 2D-QSAR and 3D-QSAR Analyses for EGFR Inhibitors

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Manman Zhao ◽  
Lin Wang ◽  
Linfeng Zheng ◽  
Mengying Zhang ◽  
Chun Qiu ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
3D Qsar ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Egfr Inhibitors ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
2D Qsar ◽  
Structure Activity

Epidermal growth factor receptor (EGFR) is an important target for cancer therapy. In this study, EGFR inhibitors were investigated to build a two-dimensional quantitative structure-activity relationship (2D-QSAR) model and a three-dimensional quantitative structure-activity relationship (3D-QSAR) model. In the 2D-QSAR model, the support vector machine (SVM) classifier combined with the feature selection method was applied to predict whether a compound was an EGFR inhibitor. As a result, the prediction accuracy of the 2D-QSAR model was 98.99% by using tenfold cross-validation test and 97.67% by using independent set test. Then, in the 3D-QSAR model, the model with q2=0.565 (cross-validated correlation coefficient) and r2=0.888 (non-cross-validated correlation coefficient) was built to predict the activity of EGFR inhibitors. The mean absolute error (MAE) of the training set and test set was 0.308 log units and 0.526 log units, respectively. In addition, molecular docking was also employed to investigate the interaction between EGFR inhibitors and EGFR.

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