Abstract
Quantitative structure—activity relationships for model of estrogen receptor relative binding affinity (pRBA) have been built. These models are one-variable correlations between pRBA and optimal descriptor calculated with simplified molecular input line entry system. These models were obtained by means of the correlation balance: one subset of the training set (sub-training set) plays role of the training; the second subset (calibration set) plays role of the preliminary check of the models. Three splits into the sub-training set, calibration set, and external test set were examined. It has been shown that the correlation balance is a more robust predictor for the endpoint than classic scheme (training set–test set: without of the calibration). The statistical characteristics of the model are n = 59, r 2 = 0.8792, s = 0.643, F = 415 (sub-training set); n = 39, r 2 = 0.8805, s = 0.637, F = 273 (calibration set); and n = 31, r 2 = 0.8132, s = 0.781, F = 126 (test set).
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Acknowledgments
The authors express their gratitude to OSIRIS for financial support, to Dr. L. Cappellini, Dr. G. Bianchi, and Dr. R. Bagnati for valuable consultations on the computer science, and to J. Baggott for English editing.
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Toropov, A.A., Toropova, A.P., Diaza, R.G. et al. SMILES-based optimal descriptors: QSAR modeling of estrogen receptor binding affinity by correlation balance. Struct Chem 23, 529–544 (2012). https://doi.org/10.1007/s11224-011-9892-y
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DOI: https://doi.org/10.1007/s11224-011-9892-y