In silico toxicity evaluation of dioxins using structure–activity relationship (SAR) and two-dimensional quantitative structure–activity relationship (2D-QSAR)

2019 ◽  
Vol 93 (11) ◽  
pp. 3207-3218 ◽  
Author(s):  
Hong Yang ◽  
Zhe Du ◽  
Wen-Juan Lv ◽  
Xiao-Yun Zhang ◽  
Hong-Lin Zhai
Keyword(s):  
In Silico ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Two Dimensional ◽  
Quantitative Structure ◽  
Toxicity Evaluation ◽  
2D Qsar ◽  
Structure Activity ◽  
In Silico Toxicity

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.

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.

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