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论文类型：期刊论文

发表时间：2008-05-01

发表刊物：MOLECULAR DIVERSITY

收录刊物：SCIE

卷号：12

期号：2

页面范围：93-102

ISSN号：1381-1991

关键字：estrogen receptor(alpha) modulators; prediction; neural networks; QSAR

摘要：The estrogen receptor (ER), an important drug target for the therapy of breast cancers, received a great deal of attention during recent years. This work aimed at finding more potent and selective ER modulators through the investigations of multiple ligand-receptor interactions by exploring the relationship between the experimental and predicted pIC(50) values using in silico methods. A Bayesian-regularized neural network combined with principal component analysis has been conducted on a set of ER(alpha) modulators (127 molecules), resulting in the correlation coefficients of 0.91 +/- 0.02, 0.87 +/- 0.04 and 0.90 +/- 0.02 for the training set (64 molecules), cross-validation set (32 molecules) and independent test (31 molecules), respectively. Meanwhile, a multiple linear regression (MLR) method has also been applied in order to explore the most important variables related to the biological activities. The proposed MLR model obtains a reasonable predictivity of pIC(50) (R = 0.72, Q = 0.79) and makes use of four molecular descriptors, namely, Xvch6, nelem, SsssCH and SaaN. All these results prove the reliabilities of the in silico models, which should be useful not only for the screening but also for the rational design of novel ER(alpha) modulators with improved potency.