3D QSAR Pharmacophore Modeling for c-Met Kinase Inhibitors

2012 ◽  
Vol 8 (6) ◽  
pp. 1117-1125
Author(s):  
Dandan Huang ◽  
Xiaoyun Zhu ◽  
Chunlei Tang ◽  
Yicheng Mei ◽  
Wei Chen ◽  
...  
2020 ◽  
Vol 31 (5) ◽  
pp. 1675-1690
Author(s):  
Bhushan D. Varpe ◽  
Shailaja B. Jadhav ◽  
Bandoo C. Chatale ◽  
Anil S. Mali ◽  
Shravan Y. Jadhav ◽  
...  

2020 ◽  
Author(s):  
BN Acharya

This study describes screening of DrugBank library for approved drugs by pharmacophore modeling and receptor-ligand docking. A 3D-QSAR model was generated on the<br>inhibition constants (Ki AutoDock ) determined by AutoDock. This 3D-QSAR model was statistically validated by Fischer’s randomization test and further evaluated by a test set<br>comprising 75 molecules. Ki AutoDock values of 49 molecules were predicted correctly by the 3D-QSAR model. The validated 3D-QSAR model was used for screening of DrugBank library for approved molecules to identify potential molecules against novel SARS corona virus-2 (SARS-CoV-2). Ten out of 40 the shortlisted molecules were kinase inhibitors.


2012 ◽  
Vol 8 (6) ◽  
pp. 1117-1125
Author(s):  
Dandan Huang ◽  
Xiaoyun Zhu ◽  
Chunlei Tang ◽  
Yicheng Mei ◽  
Wei Chen ◽  
...  

2020 ◽  
Author(s):  
BN Acharya

This study describes screening of DrugBank library for approved drugs by pharmacophore modeling and receptor-ligand docking. A 3D-QSAR model was generated on the<br>inhibition constants (Ki AutoDock ) determined by AutoDock. This 3D-QSAR model was statistically validated by Fischer’s randomization test and further evaluated by a test set<br>comprising 75 molecules. Ki AutoDock values of 49 molecules were predicted correctly by the 3D-QSAR model. The validated 3D-QSAR model was used for screening of DrugBank library for approved molecules to identify potential molecules against novel SARS corona virus-2 (SARS-CoV-2). Ten out of 40 the shortlisted molecules were kinase inhibitors.


Author(s):  
Lalita Dahiya ◽  
Manoj Kumar Mahapatra ◽  
Ramandeep Kaur ◽  
Vipin Kumar ◽  
Manoj Kumar

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