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Polishchuk Pavlo Ph.D., M.Sc.

Journals

EasyDock: customizable and scalable docking tool. Journal of Cheminformatics. 2023, 15(1), 102, ISSN: 1758-2946, PMID: 37915072,
ZANKOV, D., T. MADZHIDOV, P. POLISHCHUK, P. SIDOROV, A. VARNEK
Multi-Instance Learning Approach to the Modeling of Enantioselectivity of Conformationally Flexible Organic Catalysts. Journal of Chemical Information and Modeling. 2023, ISSN: 1549-9596, PMID: 37902548,
JURÁŠEK, M., J. ŘEHULKA, L. HRUBÁ, A. IVANOVA (NIKONENKO), S. GURSKÁ, O. MOKSHYNA, P. TROUSIL, K. HUML, P. POLISHCHUK, M. HAJDÚCH, P. DRAŠAR, P. DŽUBÁK
Triazole-based estradiol dimers prepared via CuAAC from 17α-ethinyl estradiol with five-atom linkers causing G2/M arrest and tubulin inhibition. Bioorganic Chemistry. 2023, 131(2023), 106334, ISSN: 0045-2068, PMID: 36592487, DATA.

Books & book chapters

Multi-instance Learning for Structure-Activity Modeling for Molecular Properties, 1.vyd, Kazan, Springer, 2021, 7, 62-71, Dedication: RFMEFI57518X0177, ISBN: 978-3-030-39574-2,
TINKOV, O., P. POLISHCHUK, V. GRIGOREV, Y. POROZOV
The Cross-Interpretation of QSAR Toxicological Models, 1.vyd., Springer, Cham, 2020, 262-273, ISBN: 978-3-030-57820-6,
POLISHCHUK, P., E. MOKSHYNA, A. KOSINSKAYA, A. MUATS, M. KULINSKY, O. TINKOV, L. OGNICHENKO, T. KHRISTOVA, A. ARTEMENKO, V. KUZMIN
Structural, Physicochemical and Stereochemical Interpretation of QSAR Models Based on Simplex Representation of Molecular Structure, 1st, Springer International Publishing, 2018, 4, 107-147, ISBN: 978-3-319-56849-2,

Doctoral mentorship

Matveieva Mariia

Automatic mining of relationships between structure and activity in chemical databases

Status: Graduated from 2016 to 2022.
Minibaeva Guzel

De novo design of syntheticaly feasible molecules

Status: Ongoing from 2022.
Ivanova (Nikonenko) Aleksandra

In silico design of compounds with desired properties

Status: Ongoing from 2019.

Master mentorship

Švec Dávid

Data-driven optimization of compound properties and exploration of a chemical space

Status: Graduated from 2016 to 2019.

Open positions

Project: Development of 3D pharmacophore signatures and their application in the design of anticancer drugs
Supervisors: Polishchuk Pavlo Ph.D., M.Sc.
Available: 1
Intended for: Doctoral training
Summary: 1 place in the face-to-face form of study
Project: Development of 3D pharmacophore signatures and their applications to drug design
Supervisors: Polishchuk Pavlo Ph.D., M.Sc.
Available: 1
Intended for: Doctoral training
Summary: Pharmacophore modeling is a powerful approach to encode possible protein-ligand interactions and searching of new promising compounds in large libraries. So far, almost all available software for pharmacophore modeling is proprietary and implemented approaches have some limitations to efficiently work with big data. Within this study a new approach to represent 3D pharmacophores as hashes will be implemented. This representation will make it possible to quickly identify similar pharmacophores in large data sets. This property can be used to develop a new alignment free approach to ligand-based pharmacophore modeling. The developed 3D pharmacophore hashes will help to identify representative pharmacophores retrieved from molecular dynamic simulation of protein-ligand complexes. These developments will increase success rates of future screening campaigns and should be implemented in open-source software.
Project: In silico design of compounds with desired properties
Supervisors: Polishchuk Pavlo Ph.D., M.Sc.
Available: 2
Intended for: Doctoral training
Summary: One of the main goals of chemoinformatics is development of new compounds with desired properties or activities. Many de novo design approaches were suggested so far. The designed compounds should satisfy multiple criteria, e.g. synthetic accessibility, novelty, diversity, selectivity, etc. Generators of chemical structures satisfying these criteria are a core of all de novo design approaches. Available approaches often result in synthetically hardly accessible structures or limit their diversity and novelty. Within this study a new fragment-based approach for structure generation will be implemented which will result in chemically valid structures and will provide flexible control over their diversity, novelty and synthetic accessibility. This will be used for development of de novo design approaches based on molecular docking, pharmacophore modeling to generate compounds which will be able to fit to a binding site of a given protein. This can be used for development of novel compounds and for optimization of structures of available ligands. Developed approaches should be implemented in open-source software tools.
Project: New chemoinformatics approaches to fragment-based drug discovery
Supervisors: Polishchuk Pavlo Ph.D., M.Sc.
Available: 1
Intended for: Doctoral training
Project: Development of 3D pharmacophore signatures and their application in anticancer drug design
Supervisors: Polishchuk Pavlo Ph.D., M.Sc.
Available: 1
Intended for: Doctoral training
Project: Modifications of biologically active molecules leading to improvement of their pharmacological properties
Supervisors: Urban Milan Ph.D., Ranc Václav Ph.D., Polishchuk Pavlo Ph.D., M.Sc.
Available: 3
Intended for: Doctoral training
Project: Ligand- and structure-based modeling of biologically active compounds
Supervisors: Polishchuk Pavlo Ph.D., M.Sc.
Available: 1
Intended for: Doctoral training
Project: Fragment-based de novo design using pharmacophore models
Supervisors: Polishchuk Pavlo Ph.D., M.Sc.
Available: 1
Intended for: Master training
Project: Novel 3D pharmacophore representation for machine learning
Supervisors: Polishchuk Pavlo Ph.D., M.Sc.
Available: 1
Intended for: Master training
Project: Computationally guided optimization of compound properties
Supervisors: Polishchuk Pavlo Ph.D., M.Sc.
Available: 1
Intended for: Master training
Project: Applicability domains in machine learning modeling
Supervisors: Polishchuk Pavlo Ph.D., M.Sc.
Available: 1
Intended for: Master training