Меню раздела

Основное меню

Преподаватели и сотрудники

Радченко Евгений Валерьевич

Занимаемые должности

ГПХ (Кафедра химии и технологии органического синтеза)

E-mail

eradchenko@muctr.ru

Сайт https://muctr.ru
Уровень образования Высшее
Квалификация

Химик

Преподаваемые дисциплины

Хемоинформатика

Методы оценки связи структура биоакт-ть

Учёная степень

Кандидат химических наук

Наименование направления подготовки и (или) специальности

Химия

Общий стаж работы 24 года (с 01.12.1995)
Стаж работы по специальности 24 года (с 01.12.1995)

Публикации

Analysis of chemical spaces: Implications for drug repurposing / A. A. Orlov, V. P. Berishvili, A. A. Nikitina et al. // In Silico Drug Design. — United States: United States, 2019. — P. 359–395. [ DOI ]

Ramified derivatives of 5-(perylen-3-ylethynyl)uracil-1-acetic acid and their antiviral properties / K. A. Sapozhnikova, N. A. Slesarchuk, A. A. Orlov et al. // RSC advances. — 2019. — Vol. 9, no. 45. — P. 26014–26023. [ DOI ]

Synthesis, molecular docking, and biological evaluation of 3-oxo-2-tolylhydrazinylidene-4,4,4-trifluorobutanoates bearing higher and natural alcohol moieties as new selective carboxylesterase inhibitors / G. F. Makhaeva, N. A. Elkina, E. V. Shchegolkov et al. // Bioorganic Chemistry. — 2019. — Vol. 91. — P. 103097. To search for effective and selective inhibitors of carboxylesterase (CES), a series of 3-oxo-2-tolylhydrazinylidene-4,4,4-trifluorobutanoates bearing higher or natural alcohol moieties was synthesized via pre-transesterification of ethyl trifluoroacetylacetate with alcohols to isolate transesterificated oxoesters as lithium salts, which were then subjected to azo coupling with tolyldiazonium chloride. Inhibitory activity against porcine liver CES, along with two structurally related serine hydrolases, acetylcholinesterase and butyrylcholinesterase, were investigated using enzyme kinetics and molecular docking. Kinetics studies demonstrated that the tested keto-esters are reversible and selective mixed-type CES inhibitors. Analysis of X-ray crystallographic data together with our IR and NMR spectra and QM calculations indicated that the Z-isomers were the most stable. The kinetic data were well explained by the molecular docking results of the Z-isomers, which showed specific binding of the compounds in the CES catalytic active site with carbonyl oxygen atoms in the oxyanion hole and non-specific binding outside it. Some compounds were studied as inhibitors of the main human isozymes involved in biotransformation of ester-containing drugs, hCES1 and hCES2. Esters of geraniol (3d) and adamantol (3e) proved to be highly active and selective inhibitors of hCES2, inhibiting the enzyme in the nanomolar range, whereas esters of borneol (3f) and isoborneol (3g) were more active and selective against hCES1. Computational ADMET studies revealed that all test compounds had excellent intestinal absorption, medium blood-brain barrier permeability, and low hERG liability risks. Moreover, all test compounds possessed radical-scavenging properties and low acute toxicity. Overall, the results indicate that members of this novel series of esters have the potential to be good candidates as hCES1 or hCES2 inhibitors for biomedicinal applications. [ DOI ]

Time-domain analysis of molecular dynamics trajectories using deep neural networks: Application to activity ranking of tankyrase inhibitors / V. P. Berishvili, V. O. Perkin, A. E. Voronkov et al. // Journal of Chemical Information and Modeling. — 2019. — Vol. 59, no. 8. — P. 3519–3532. Molecular dynamics simulations provide valuable insights into the behavior of molecular systems. Extending the recent trend of using machine learning techniques to predict physicochemical properties from molecular dynamics data, we propose to consider the trajectories as multidimensional time series represented by 2D tensors containing the ligand–protein interaction descriptor values for each time step. Similar in structure to the time series encountered in modern approaches for signal, speech, and natural language processing, these time series can be directly analyzed using long short-term memory (LSTM) recurrent neural networks or convolutional neural networks (CNNs). The predictive regression models for the ligand–protein affinity were built for a subset of the PDBbind v.2017 database and applied to inhibitors of tankyrase, an enzyme of the poly(ADP-ribose)-polymerase (PARP) family that can be used in the treatment of colorectal cancer. As an additional test set, a subset of the Community Structure–Activity Resource (CSAR) data set was used. For comparison, the random forest and simple neural network models based on the crystal pose or the trajectory-averaged descriptors were used, as well as the commonly employed docking and molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) scores. Convolutional neural networks based on the 2D tensors of ligand–protein interaction descriptors for short (2 ns) trajectories provide the best accuracy and predictive power, reaching the Spearman rank correlation coefficient of 0.73 and Pearson correlation coefficient of 0.70 for the tankyrase test set. Taking into account the recent increase in computational power of modern GPUs and relatively low computational complexity of the proposed approach, it can be used as an advanced virtual screening filter for compound prioritization. [ DOI ]

Influence of descriptor implementation on compound ranking based on multi-parameter assessment / E. A. Sosnina, D. I. Osolodkin, E. V. Radchenko et al. // Journal of Chemical Information and Modeling. — 2018. — Vol. 58, no. 5. — P. 1083–1093. [ DOI ]

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