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Short Communication Open Access
Broad range of structurally diverse alkylphenols has been found to be considerably potential estrogenic agents in combating estrogen-linked pathologies, but their mechanism of action in mimicking responses of endogenous hormones is still to be understood. The present work explores pharmacophore signals of some varied alkylphenols and predicts estrogenic activities through generated linear relations implementing theoretical molecular modeling techniques. The binding affinity to estrogen receptor of alkylphenols has been modeled investigating large data set of whole molecular and atomic descriptors. Univariate and multivariate relationships were estimated using correlation analysis and statistical significance of the generated relations assessed. The predictive ability of the generated models was further verified using 'Leave-One-Out' cross validation. The relationships with molecular properties could be developed with a maximum correlation exceeding 94%, with explained variance as high as 87% and cross-validated variances >0.8. It was inferred that increased molecular bulk, enhanced molecular ionization potential, presence of electron donating groups in para position and branched chain terminal atoms might have influence on binding affinity to the receptor.