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

Stanojević, Mark (author), Vračko, Marjan (author), Sollner Dolenc, Marija (author)

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.

• Language: English
• Keywords: endocrine disruptions, binding to glucocorticoid receptor, in silico classification, counterpropagation artificial neural networks, DRAGON descriptors
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FFA - Faculty of Pharmacy
• Publication status: Published
• Publication version: Version of Record
• Year: 2023
• Number of pages: 6 str.
• Numbering: Vol. 336, art. 139147
• PID: 20.500.12556/RUL-148284
• UDC: 612.43:616.4
• ISSN on article: 0045-6535
• DOI: 10.1016/j.chemosphere.2023.139147
• COBISS.SI-ID: 155559171
• Publication date in RUL: 09.08.2023

Record is a part of a journal

• Title: Chemosphere
• Shortened title: Chemosphere
• Publisher: Elsevier
• ISSN: 0045-6535
• COBISS.SI-ID: 25213696

Licences

• License: CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
• Link: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Description: The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.

Secondary language

• Language: Slovenian
• Keywords: endokrine motnje, vezave na glukokortikoidni receptor, klasifikacija in silico, umetne nevronske mreže, deskriptorji DRAGON, hormonski motilci, endokrinologija