title: [Molecular docking and 3D-QSAR on 16alpha,17alpha-cycloalkanoprogesterone analogues as progesterone receptor ligands].
abstract: A series of 42 steroid ligands was used to predict a binding affinity to progesterone receptor. The molecules were the derivatives of 16alpha,17alpha-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.
authors: I V
authors: IV
authors: Fediushkina
authors: V S
authors: VS
authors: Skvortsov
authors: I V
authors: IV
authors: Romero Reyes
authors: I S
authors: IS
authors: Levina
mesh_terms: D000818:Animals; D006801:Humans; D008024:Ligands; D062105:Molecular Docking Simulation / Q000379:methods* / Q000706:statistics & numerical data; D015394:Molecular Structure; D011374:Progesterone / Q000031:analogs & derivatives* / Q000737:chemistry*; D021281:Quantitative Structure-Activity Relationship; D011980:Receptors, Progesterone / Q000819:agonists*
chemical_list: D008024:Ligands; D011980:Receptors, Progesterone; D011374:Progesterone