scholarly journals Modeling GC-ECD retention times of pentafluorobenzyl derivatives of phenol by using artificial neural networks

2008 ◽  
Vol 31 (21) ◽  
pp. 3788-3795 ◽  
Author(s):  
Karim Asadpour-Zeynali ◽  
Naser Jalili-Jahani
2013 ◽  
Vol 59 (6) ◽  
pp. 622-635 ◽  
Author(s):  
I.V. Fedyushkina ◽  
V.S. Skvortsov ◽  
I.V. Romero Reyes ◽  
I.S. Levina

A series of 42 steroid ligands was used to predict a binding affinity to progesterone receptor. The molecules were the derivatives of 16a,17a-cycloalkanoprogesterones. Different methods of prediction were used and analyzed such as CoMFA and artificial neural networks. The best result (Q2=0.91) was obtained for a combination of molecular docking, molecular dynamics simulation and artificial neural networks. A predictive power of the model was validated by a group of 8 pentarans synthesized separately and tested in vitro (R2test=0.77). This model can be used to determine the affinity level of the ligand to progesterone receptor and accurate ranking of binding compounds.


Drug Research ◽  
2013 ◽  
Vol 64 (03) ◽  
pp. 151-158 ◽  
Author(s):  
Sh. Shahsavari ◽  
G. Bagheri ◽  
R. Mahjub ◽  
R. Bagheri ◽  
M. Radmehr ◽  
...  

Author(s):  
Kobiljon Kh. Zoidov ◽  
◽  
Svetlana V. Ponomareva ◽  
Daniel I. Serebryansky ◽  
◽  
...  

2012 ◽  
Vol 3 (2) ◽  
pp. 48-50
Author(s):  
Ana Isabel Velasco Fernández ◽  
◽  
Ricardo José Rejas Muslera ◽  
Juan Padilla Fernández-Vega ◽  
María Isabel Cepeda González

Sign in / Sign up

Export Citation Format

Share Document