Development of in silico classification models for binding affinity to the glucocorticoid receptor.

Autor: Stanojević M; Bisafe Doo, V Kladeh 11c, 1000, Ljubljana, Slovenia; University of Ljubljana, Faculty of Pharmacy, Aškerčeva cesta 7, 1000, Ljubljana, Slovenia. Electronic address: mark.stanojevic@bisafe.si., Vračko M; National Institute of Chemistry, Hajdrihova 19, 1000, Ljubljana, Slovenia. Electronic address: marjan.vracko@ki.si., Sollner Dolenc M; University of Ljubljana, Faculty of Pharmacy, Aškerčeva cesta 7, 1000, Ljubljana, Slovenia. Electronic address: marija.sollner@ffa.uni-lj.si.
Jazyk: angličtina
Zdroj: Chemosphere [Chemosphere] 2023 Sep; Vol. 336, pp. 139147. Date of Electronic Publication: 2023 Jun 08.
DOI: 10.1016/j.chemosphere.2023.139147
Abstrakt: The endocrine disrupting properties of chemicals acting through the glucocorticoid receptor (GR) have attracted considerable interest. Since there are few data for most chemicals on their endocrine properties in silico approaches seem to be the most appropriate tool for screening and prioritizing chemicals for planning further experiments. In this work, we developed classification models for binding affinity to the glucocorticoid receptor using the counterpropagation artificial neural network method. We considered two series of 142 and 182 compounds and their binding affinity to the glucocorticoid receptor as agonists and antagonists, respectively. The compounds belong to different chemical classes. The compounds were represented by a set of descriptors calculated with the DRAGON program. The clustering structure of sets was studied with standard principal component method. A weak separation between binders and non-binders was found. Another classification model was developed using the counterpropagation artificial neural network method (CPANN). The final classification models developed were well balanced and showed a high level of accuracy, with 85.7% of GR agonist and 78.9% of GR antagonist correctly assigned in leave-one-out cross-validation.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)
Databáze: MEDLINE
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