informИнформатикаInformatics1816-03012617-6963UIIP NASB10.37661/1816-0301-2023-20-3-7-20inform-1259Research ArticleБИОИНФОРМАТИКАBIOINFORMATICSГенеративная нейронная сеть на основе модели гетероэнкодера для de novo дизайна потенциальных противоопухолевых препаратов: применение к Bcr-Abl тирозинкиназеA generative neural network based on a hetero-encoder model for de novo design of potential anticancer drugs: application to Bcr-Abl tyrosine kinasehttps://orcid.org/0000-0003-3968-6209КарпенкоА. Д.KarpenkoA. D.

Карпенко Анна Дмитриевна - научный сотрудник.

ул. Сурганова, 6, Минск, 220012

Anna D. Karpenko - Researcher, The United Institute of Informatics Problems of the National Academy of Sciences of Belarus.

Surganova st., 6, Minsk, 220012

rfe.karpenko@gmail.comВойткоТ. Д.VaitkoT. D.

Войтко Тимофей Дмитриевич – студент.

пр. Независимости, 4, Минск, 220030

Timofey D. Vaitko - Student, Belarusian State University.

Nezavisimosti av., 4, Minsk, 220030

timvaitko@gmail.comhttps://orcid.org/0000-0001-5970-4852ТузиковА. В.TuzikovA. V.

Тузиков Александр Васильевич - член-корреспондент, доктор физико-математических наук, профессор, заведующий лабораторией математической кибернетики.

ул. Сурганова, 6, Минск, 220012

Alexander V. Tuzikov - Corresponding Member, D. Sc. (Phys.-Math.), Prof., Head of the Laboratory of Mathematical Cybernetics, The United Institute of Informatics Problems of the National Academy of Sciences of Belarus.

Surganova st., 6, Minsk, 220012

tuzikov@newman.bas-net.byАндриановА. М.AndrianovA. M.

Андрианов Александр Михайлович - доктор химических наук, профессор, главный научный сотрудник.

ул. Купревича, 5/2, Минск, 220084

Alexander M. Andrianov - D. Sc. (Chem.), Prof., Chief Researcher, Institute of Bioorganic Chemistry of the National Academy of Sciences of Belarus.

Kuprevicha st., 5/2, Minsk, 220084

alexande.andriano@yandex.ruОбъединенный институт проблем информатики Национальной академии наук БеларусиThe United Institute of Informatics Problems of the National Academy of Sciences of BelarusБелорусский государственный университетBelarusian State UniversityИнститут биоорганической химии Национальной академии наук БеларусиInstitute of Bioorganic Chemistry of the National Academy of Sciences of Belarus202329092023203720Copyright © Карпенко А.Д., Войтко Т.Д., Тузиков А.В., Андрианов А.М., 20232023Карпенко А.Д., Войтко Т.Д., Тузиков А.В., Андрианов А.М.Karpenko A.D., Vaitko T.D., Tuzikov A.V., Andrianov A.M.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://inf.grid.by/jour/article/view/1259Цели

Цели. Решается задача разработки генеративной модели гетероэнкодера для компьютерного дизайна потенциальных ингибиторов Bcr-Abl тирозинкиназы - фермента, активность которого является патофизиологической причиной хронического миелоидного лейкоза.

Методы

Методы. На основе рекуррентных и полносвязных нейронных сетей прямого распространения создана генеративная модель гетероэнкодера. Проведены обучение и тестирование этой модели на наборе химических соединений, которые содержат 2-ариламинопиримид, присутствующий в качестве основного фармакофора в структурах многих низкомолекулярных ингибиторов протеинкиназ.

Результаты

Результаты. Разработанная нейронная сеть апробирована в процессе генерации широкого набора новых молекул и последующего анализа их химического сродства к Bcr-Abl тирозинкиназе методами молекулярного докинга.

Заключение

Заключение. Показано, что разработанная нейронная сеть представляет собой перспективную математическую модель для de novo дизайна малых молекул, которые потенциально активны против Bcr-Abl тирозинкиназы и могут быть использованы для разработки эффективных противоопухолевых препаратов широкого спектра действия.

Objectives

Objectives. The problem of developing a generative hetero-encoder model for computer-aided design of potential inhibitors of Bcr-Abl tyrosine kinase, an enzyme whose activity is the pathophysiological cause of chronic myeloid leukemia, is being solved.

Methods

Methods. A generative hetero-encoder model was designed based on the recurrent and fully connected neural networks of direct propagation. Training and testing of this model were carried out on a set of chemical compounds containing 2-arylaminopyrimidine, which is present as the main pharmacophore in the structures of many small-molecule inhibitors of protein kinases.

Results

Results. The developed neural network was tested in the process of generating a wide range of new molecules and subsequent analysis of their chemical affinity for Bcr-Abl tyrosine kinase using molecular docking methods.

Conclusion

Conclusion. It is shown that the developed neural network is a promising mathematical model for de novo design of small molecules which are potentially active against Bcr-Abl tyrosine kinase and can be used to develop effective broad-spectrum anticancer drugs.

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The authors declare that there are no conflicts of interest present.