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

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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 ER α 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 ER α modulators with improved potency.

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Authors and Affiliations

  1. Key Lab of Mariculture and Biotechnology, Ministry of Agriculture, Dalian Fisheries University, Dalian, China

    Yonghua Wang, Jun Ding, Yuan Wang & Yaqing Chang

  2. School of Chemical Engineering, Dalian University of Technology, Dalian, China

    Yan Li

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  1. Yonghua Wang
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  2. Yan Li
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  3. Jun Ding
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  4. Yuan Wang
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Correspondence to Yonghua Wang.

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Wang, Y., Li, Y., Ding, J. et al. Prediction of binding affinity for estrogen receptor α modulators using statistical learning approaches. Mol Divers 12, 93–102 (2008). https://doi.org/10.1007/s11030-008-9080-1

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