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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 23 года (с 01.12.1995)
Стаж работы по специальности 23 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 ]

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 ]

Machine learning classification models to improve the docking‐based screening: A case of pi3k‐tankyrase inhibitors / V. P. Berishvili, A. E. Voronkov, E. V. Radchenko, V. A. Palyulin // Molecular informatics. — 2018. — Vol. 37, no. 11. — P. 1800030. One of the major challenges in the current drug discovery is the improvement of the docking‐based virtual screening performance. It is especially important in the rational design of compounds with desired polypharmacology or selectivity profiles. To address this problem, we present a methodology for the development of target‐specific scoring functions possessing high screening power. These scoring functions were built using the machine learning methods for the dual target inhibitors of PI3Kα and tankyrase, promising targets for colorectal cancer therapy. The Deep Neural Network models achieve the external test AUC ROC values of 0.96 and 0.93 for the random split and 0.90 and 0.84 for the time‐based split of the PI3Kα and tankyrase inhibitors, respectively. In addition, the impact of the training set size and the actives/decoys ratio on the model quality was assessed. The study demonstrates that the optimized scoring functions could significantly improve the docking screening power for each individual target. This is very useful in the design of multitarget or selective drugs wherein the screening filters are applied in sequence. [ DOI ]

Radchenko E. V., Palyulin V. A., Zefirov N. S. Molecular field topology analysis (mfta) in the design of neuroprotective compounds // Computational Modeling of Drugs Against Alzheimer’s Disease. — Vol. 132 of Neuromethods. — Springer, 2018. — P. 139–159. The Molecular Field Topology Analysis (MFTA) is a QSAR method designed to model the activities mediated by small molecule binding to biotargets using the local physicochemical descriptors reflecting the major types of ligand–target interactions. A molecular supergraph provides a common frame of reference for the meaningful comparison of the properties of atoms in different structures and the visualization of their effects. The MFTA method has been successfully used in the activity and selectivity modeling, design, and virtual screening of promising ligands of various enzymes and receptors (the NMDA and AMPA receptor antagonists and modulators as well as the acetyl- and butyrylcholinesterase inhibitors are of particular interest in the design of the anti-Alzheimer and other neuroprotective compounds). The design of potential anti-Alzheimer and other neuroprotective compounds based on the MFTA structure–activity models involves the following basic steps: (1) preparation of a training set containing structures of the compounds and the experimental activity and/or selectivity values, (2) generation of a series of MFTA models using various descriptor combinations, (3) evaluation of model quality and selection of the optimal model, (4) interpretation of the model, (5) preparation of the virtual screening library, (6) prediction of the activity and/or selectivity endpoints, (7) selection of promising compounds, and (8) prediction of the relevant ADMET properties. As a result, a focused library of potential neuroprotective compounds having the desired activity/selectivity profile and acceptable ADMET properties is obtained. The workflow presented in this chapter using a case study involving the inhibitors of glycogen synthase kinase 3β is applicable to many other relevant targets and ligand classes. In addition, some of its elements may be incorporated into other virtual screening and design workflows. [ DOI ]

Molecular design, synthesis and biological evaluation of cage compound-based inhibitors of hepatitis c virus p7 ion channels / V. A. Shiryaev, E. V. Radchenko, V. A. Palyulin et al. // European Journal of Medicinal Chemistry. — 2018. — Vol. 158. — P. 214–235. Highlights: Hepatitis C virus p7 viroporin is an attractive target for the cage-based inhibitors; Potential broad-spectrum inhibitors were designed by molecular modeling and dynamics; 53 compounds were synthesized and tested for antiviral activity in BVDV model; Results demonstrate potential therapeutic usefulness and encourage detailed studies. ==== The hepatitis C caused by the hepatitis C virus (HCV) is an acute and/or chronic liver disease ranging in severity from a mild brief ailment to a serious lifelong illness that affects up to 3% of the world population and imposes significant and increasing social, economic, and humanistic burden. Over the past decade, its treatment was revolutionized by the development and introduction into clinical practice of the direct acting antiviral (DAA) agents targeting the non-structural viral proteins NS3/4A, NS5A, and NS5B. However, the current treatment options still have important limitations, thus, the development of new classes of DAAs acting on different viral targets and having better pharmacological profile is highly desirable. The hepatitis C virus p7 viroporin is a relatively small hydrophobic oligomeric viral ion channel that plays a critical role during virus assembly and maturation, making it an attractive and validated target for the development of the cage compound-based inhibitors. Using the homology modeling, molecular dynamics, and molecular docking techniques, we have built a representative set of models of the hepatitis C virus p7 ion channels (Gt1a, Gt1b, Gt1b_L20F, Gt2a, and Gt2b), analyzed the inhibitor binding sites, and identified a number of potential broad-spectrum inhibitor structures targeting them. For one promising compound, the binding to these targets was additionally confirmed and the binding modes and probable mechanisms of action were clarified by the molecular dynamics simulations. A number of compounds were synthesized, and the tests of their antiviral activity (using the BVDV model) and cytotoxicity demonstrate their potential therapeutic usefulness and encourage further more detailed studies. The proposed approach is also suitable for the design of broad-spectrum ligands interacting with other multiple labile targets including various viroporins. [ DOI ]

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