Journal
MOLECULAR DIVERSITY
Volume 12, Issue 2, Pages 93-102Publisher
SPRINGER
DOI: 10.1007/s11030-008-9080-1
Keywords
estrogen receptor(alpha) modulators; prediction; neural networks; QSAR
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Funding
- National High-Tech Research and Development Program of China [2006 AA10A411]
- Dalian University of Technology
- Dalian Fisheries University
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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.
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