SMILES-based optimal descriptors: QSAR modeling of estrogen receptor binding affinity by correlation balance

Andrey A. Toropov, Alla P. Toropova, Rodolfo Gonella Diaza, Emilio Benfenati, Giuseppina Gini

Research output: Contribution to journalArticle

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).

Original languageEnglish
Pages (from-to)529-544
Number of pages16
JournalStructural Chemistry
Volume23
Issue number2
DOIs
Publication statusPublished - Apr 2012

Keywords

  • Binding affinity
  • Correlation balance
  • Estrogen receptor
  • Optimal descriptor
  • QSAR
  • SMILES

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Physical and Theoretical Chemistry

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