Docking and 3D-QSAR (quantitative structure activity relationship) studies of flavones, the potent inhibitors of p-glycoprotein targeting the nucleotide binding domain

2011 ◽  
Vol 46 (9) ◽  
pp. 4078-4088 ◽  
Author(s):  
Gugan Kothandan ◽  
Changdev G. Gadhe ◽  
Thirumurthy Madhavan ◽  
Cheol Hee Choi ◽  
Seung Joo Cho
Keyword(s):  
3D Qsar ◽  
Nucleotide Binding ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Binding Domain ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Nucleotide Binding Domain ◽  
Structure Activity ◽  
P Glycoprotein

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.

scholarly journals A Computational Approach to Drug Discovery: Search for Chalcone Analogues as the Potential Candidates for Anti Colorectal Cancer (HT29)

2018 ◽  
Vol 17 (2) ◽  
pp. 64-74
Author(s):  
Neni FRIMAYANTI ◽  
Ihsan IKHTIARUDIN ◽  
Rahma DONA ◽  
Tiara Tri AGUSTINI ◽  
Fri MURDIYA ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Molecular Dynamic ◽  
Predictive Ability ◽  
3D Qsar ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Data Set ◽  
Structure Activity

A series of 46 chalcone derivative compounds with their inhibitory activity against colorectal cancer were used as data set for developing the quantitative structure activity relationship (QSAR). 2D QSAR and 3D QSAR models have been developed with high predictive ability with r2 and r2(CV) of 0.81 and 0.78, respectively. Results from the 2D and 3D quantitative structure activity relationship models indicate that electrostatic parameter enhanced bioactivity of the chalcone derivatives. Further, docking and molecular dynamic simulation was performed using 2wft PDB ID as the molecular target of colon cancer. Based on the docking, molecular dynamic, and biological assay, it is confirmed that compound 2, cpd 4, cpd 21, cpd 23, cpd 27, cpd 32, cpd 38, and cpd 39 show better activity (active) against colorectal cancer cells.

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