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Computational study of estrogen receptor-alpha antagonist with three-dimensional quantitative structure-activity relationship, support vector regression, and linear regression methods.

ABSTRACT: 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?.

SUBMITTER: Chang YH 

PROVIDER: S-EPMC4245501 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Computational study of estrogen receptor-alpha antagonist with three-dimensional quantitative structure-activity relationship, support vector regression, and linear regression methods.

Chang Ying-Hsin YH   Chen Jun-Yan JY   Hor Chiou-Yi CY   Chuang Yu-Chung YC   Yang Chang-Biau CB   Yang Chia-Ning CN  

International journal of medicinal chemistry 20130514

Human estrogen receptor (ER) isoforms, ERα and ERβ, have long been an important focus in the field of biology. To better understand the structural features associated with the binding of ERα ligands to ERα 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/d  ...[more]

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