1. Orekhovich Institute of Biomedical Chemistry of Russian Academy of Medical Sciences 2. N.D. Zelinsky Institute of Organic Chemistry of Russian Academy of Sciences
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.
Fedyushkina I.V., Skvortsov V.S., Romero Reyes I.V., Levina I.S. (2013) Molecular docking and 3D-QSAR on 16a,17a-cycloalkanoprogesterone analogues as progesterone receptor ligands. Biomeditsinskaya Khimiya, 59(6), 622-635.
Fedyushkina I.V. et al. Molecular docking and 3D-QSAR on 16a,17a-cycloalkanoprogesterone analogues as progesterone receptor ligands // Biomeditsinskaya Khimiya. - 2013. - V. 59. -N 6. - P. 622-635.
Fedyushkina I.V. et al., "Molecular docking and 3D-QSAR on 16a,17a-cycloalkanoprogesterone analogues as progesterone receptor ligands." Biomeditsinskaya Khimiya 59.6 (2013): 622-635.
Fedyushkina, I. V., Skvortsov, V. S., Romero, Reyes, I. V., Levina, I. S. (2013). Molecular docking and 3D-QSAR on 16a,17a-cycloalkanoprogesterone analogues as progesterone receptor ligands. Biomeditsinskaya Khimiya, 59(6), 622-635.
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