title: Ð¡ÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ ÑÐ°ÑÑÐµÑÐ½ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð¾Ð»ÐµÐºÑÐ»ÑÑÐ½Ð¾Ð³Ð¾ Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð² ÑÐ°Ð¼ÐºÐ°Ñ ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ð¸Ñ Ð½ÐµÐºÐ¾Ð²Ð°Ð»ÐµÐ½ÑÐ½ÑÑ Ð²Ð·Ð°Ð¸Ð¼Ð¾Ð´ÐµÐ¹ÑÑÐ²Ð¸Ð¹ Ð² ÐºÐ¾Ð¼Ð¿Ð»ÐµÐºÑÐ°Ñ Ð±ÐµÐ»ÐºÐ¾Ð²Ð¾Ð¹ Ð¿ÑÐ¸ÑÐ¾Ð´Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Comparison of computational methods of molecular modeling in the framework of consideration of non-covalent interactions in complexes of protein nature
Creators: name: Ð¢Ð¾ÐºÐ°Ð½Ñ, ÐÐ¸Ð½Ð´Ð°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2021
Subjects: Ð¼Ð¾Ð»ÐµÐºÑÐ»ÑÑÐ½Ð¾Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
ÐºÐ²Ð°Ð½ÑÐ¾Ð²Ð°Ñ Ñ Ð¸Ð¼Ð¸Ñ
Ð½ÐµÐºÐ¾Ð²Ð°Ð»ÐµÐ½ÑÐ½ÑÐµ Ð²Ð·Ð°Ð¸Ð¼Ð¾Ð´ÐµÐ¹ÑÑÐ²Ð¸Ñ
ccsd(t)
Ð´Ð¾ÐºÐ¸Ð½Ð³
Ð°ÑÑÐ¸Ð½Ð½Ð¾ÑÑÑ ÑÐ²ÑÐ·ÑÐ²Ð°Ð½Ð¸Ñ
molecular modeling
quantum chemistry
noncovalent interactions
ccsd (t)
dooking
binding affinity

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description: The subject of the graduate qualification work is âComparison of computational methods of molecular modeling in the framework of consideration of non-covalent interactions in complexes of protein natureâ. This work is devoted to the consideration of the processes of calculating the formation energy of receptor-ligand complexes by the methods of quantum chemistry. At the current stages of the development of pharmaceuticals, this is a key stage that allows one to selectively select the most stable structures in terms of complexation energy. The relevance of this work is justified by the significantly increasing tendency in the scientific community to search for pharmaceuticals for the treatment of certain diseases using molecular modeling methods, as well as the general desire to increase the accuracy of the methods used. Within the framework of this work, a comparative assessment of molecular modeling methods was carried out by searching and studying the relevant literature. The problem of increasing the computational accuracy with minimizing the time required to assess the main components of the complexation energy of receptor-ligand-type structures is considered. The database of spatial structures of pharmaceuticals of the Russian Medicinal Register has been supplemented and docked with the selected target protein. The obtained extrapolation results demonstrate that the constructed model based on the MP2 and DFTD3 methods using machine learning gives a fairly good approximation to the results of the CCSD (T) calculation. The obtained docking results can potentially be used as a basis for further laboratory studies.