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In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
Published on: August 28, 2019
Quantitative structure-activity relationships for a series of selective estrogen receptor-beta modulators
L B Salum1, I Polikarpov, A D Andricopulo
1Laboratório de Química Medicinal e Computacional, Centro de Biotecnologia Molecular Estrutural, Instituto de Física de São Carlos, Universidade de São Paulo, São Carlos-SP, Brazil.
Abstract:
The estrogen receptor-beta subtype (ERbeta) is an attractive drug target for the development of novel therapeutic agents for hormone replacement therapy. Hologram quantitative structure-activity relationships (HQSAR) were conducted on a series of 6-phenylnaphthalene and 2-phenylquinoline derivatives, employing values of ERbeta binding affinity. A training set of 65 compounds served to derive the models. The best statistical HQSAR model (q(2) = 0.73 and r(2) = 0.91) was generated using atoms, bonds, connections and donor and acceptor as fragment distinction parameters, and fragment size default (4-7) with hologram length of 199. The model was used to predict the binding affinity of an external test set of 16 compounds, and the predicted values were in good agreement with the experimental results. The final HQSAR model and the information obtained from 2D contribution maps should be useful for the design of novel ERbeta modulators having improved affinity.
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