PubMed | Zuoying Branch of Kaohsiung Armed Forces General Hospital 813, National Sun Yat - sen University and National University of Kaohsiung
Type: | Journal: International journal of medicinal chemistry | Year: 2014
Human estrogen receptor (ER) isoforms, ER and ER, have long been an important focus in the field of biology. To better understand the structural features associated with the binding of ER ligands to ER and modulate their function, several QSAR models, including CoMFA, CoMSIA, SVR, and LR methods, have been employed to predict the inhibitory activity of 68 raloxifene derivatives. In the SVR and LR modeling, 11 descriptors were selected through feature ranking and sequential feature addition/deletion to generate equations to predict the inhibitory activity toward ER. Among four descriptors that constantly appear in various generated equations, two agree with CoMFA and CoMSIA steric fields and another two can be correlated to a calculated electrostatic potential of ER.