20.500.12556/RUL-148284 Development of in silico classification models for binding affinity to the glucocorticoid receptor 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. endocrine disruptions binding to glucocorticoid receptor in silico classification counterpropagation artificial neural networks DRAGON descriptors endokrine motnje vezave na glukokortikoidni receptor klasifikacija in silico umetne nevronske mreže deskriptorji DRAGON hormonski motilci endokrinologija true false true Angleški jezik Slovenski jezik Članek v reviji 2023-08-09 15:17:31 2023-08-09 15:17:38 2023-08-10 03:36:57 0000-00-00 00:00:00 2023 0 0 6 str. Vol. 336, art. 139147 2023 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 612.43:616.4 0045-6535 10.1016/j.chemosphere.2023.139147 155559171 1-s2.0-S0045653523014145-main.pdf 1-s2.0-S0045653523014145-main.pdf 1 2E3668EEC65830424B8CE9FD63ECA9DD a846c45b95b79ffc602858ea0e07a9ab1f1b0f08a90aff6ae3fc10648599a627 51f8b2d7-36b7-11ee-aeaf-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=172374 https://www.sciencedirect.com/science/article/pii/S0045653523014145 1 144687e4-36b7-11ee-aeaf-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=172373 Fakulteta za farmacijo 0 0 0