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Polishchuk Pavlo Ph.D., M.Sc.
About me
I'm a chemoinformatician mainly involved in medicinal chemistry projects of development of new drugs. Author of approaches and open-source software for:
- representation of chemical compounds, their mixtures, quasi-mixtures and chemical reactions for modeling purposes;
- interpretation of QSAR models to retrieve SAR knowledge and use for drug design;
- 3D pharmacophore hashes, ligand-based and MD-based pharmacophore modeling;
- de novo structure generation;
- multi-instance learning.
Author of 5 book chapters and more than 50 journal papers.
Research interests: Medicinal and bioorganic chemistry, molecular and drug design, chemoinformatics, machine learning and data mining, programming.
Web-site: http://qsar4u.com
Journals
EasyDock: customizable and scalable docking tool.
Journal of Cheminformatics.
2023,
15(1),
102,
ISSN: 1758-2946,
PMID: 37915072,
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,
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,
The Cross-Interpretation of QSAR Toxicological Models,
1.vyd.,
Springer, Cham,
2020,
262-273,
ISBN: 978-3-030-57820-6,
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.
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 |