NDLI: Prediction of binding affinity for estrogen receptor alpha modulators using statistical learning approaches.

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Prediction of binding affinity for estrogen receptor alpha modulators using statistical learning approaches.

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Prediction of binding affinity for estrogen receptor alpha modulators using statistical learning approaches.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Wang, Yonghua Li, Yan Ding, Jun Wang, Yuan Chang, Yaqing
Description	Country affiliation: China Author Affiliation: Wang Y ( Key Lab of Mariculture and Biotechnology, Ministry of Agriculture, Dalian Fisheries University, Dalian, China. yhwang@dlfu.edu.cn)
Abstract	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 pIC50 values using in silico methods. A Bayesian-regularized neural network combined with principal component analysis has been conducted on a set of ERalpha 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 pIC50 (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 ERalpha modulators with improved potency.
File Format	HTM / HTML
ISSN	13811991
Issue Number	2
Volume Number	12
e-ISSN	1573501X
Journal	Molecular Diversity
Language	English
Publisher	Springer
Publisher Date	2008-05-01
Publisher Place	Netherlands
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Discipline Molecular Biology Estrogen Receptor Modulators Chemistry Estrogen Receptor Alpha Bayes Theorem Computer Simulation Inhibitory Concentration 50 Likelihood Functions Models, Chemical Neural Networks (computer) Principal Component Analysis Quantitative Structure-activity Relationship Journal Article Research Support, Non-u.s. Gov't
Content Type	Text
Resource Type	Article
Subject	Organic Chemistry Medicine Drug Discovery Molecular Biology Physical and Theoretical Chemistry Information Systems Catalysis Inorganic Chemistry

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