Molecular Diversity

, 12:93

Prediction of binding affinity for estrogen receptorα modulators using statistical learning approaches

  • Yonghua Wang
  • Yan Li
  • Jun Ding
  • Yuan Wang
  • Yaqing Chang
Full Length Paper

DOI: 10.1007/s11030-008-9080-1

Cite this article as:
Wang, Y., Li, Y., Ding, J. et al. Mol Divers (2008) 12: 93. doi:10.1007/s11030-008-9080-1

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 ERα 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 ERα modulators with improved potency.

Keywords

Estrogen receptorα modulatorsPredictionNeural networksQSAR

Supplementary material

11030_2008_9080_MOESM1_ESM.xls (2.3 mb)
ESM 1 (XLS 780 kb)

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • Yonghua Wang
    • 1
  • Yan Li
    • 2
  • Jun Ding
    • 1
  • Yuan Wang
    • 1
  • Yaqing Chang
    • 1
  1. 1.Key Lab of Mariculture and Biotechnology, Ministry of AgricultureDalian Fisheries UniversityDalianChina
  2. 2.School of Chemical EngineeringDalian University of TechnologyDalianChina