Sabina Podlewska (Smusz), PhD

phone: +4812 66 23 301

Research interests:

  • computer-aided drug design
  • virtual screening
  • machine learning
  • docking results analysis
  • GPCR
  • molecular dynamics simulations

 Scientific career:

  • 04.2023 – habilitation, Jagiellonian University Medical College, Faculty of Pharmacy, discipline: pharmaceutical sciences
  • 10.2012–10.2016 Jagiellonian University, Faculty of Chemistry, PhD studies, scope of study: study on machine learning algorithms with a focus on its application in virtual screening (VS) tasks, together with research on determining an optimal representation of compounds for the VS purposes: obtained degree: Doctor of Philosophy (10.2016; cum laude)
  • from 02.08.2010  Institute of Pharmacology Polish Academy of Sciences, Department of Medicinal Chemistry, engineering-technical position
  • 2010–2012 Jagiellonian University, Faculty of Chemistry, the second level studies, course: chemistry, specialty: biological chemistry, obtained degree: Master of Science (06.2012)
  • 2008–2011 AGH University of Science and Technology, Faculty of Applied Mathematics, the first level studies, course: mathematics, obtained degree: Bachelor of Science (07.2011)
  • 2007–2010 Jagiellonian University, Faculty of Chemistry, the first level studies, course: chemistry, obtained degree: Bachelor of Science (06.2010)

Publications:

  1. Jamrozik, E.; Śmieja, M.; Podlewska, S. ADMET-PrInt: Evaluation of ADMET Properties: Prediction and Interpretation. J. Chem. Inf. Model. 2024, 64, 1425-1432.
  2. Pyka, P.; Haberek, W.; Więcek , M.; Szymanska, E.; Ali, W.; Cios, A.; Jastrzębska-Więsek, M.; Satała, G.; Podlewska, S.; Di Giacomo, S.; Di Sotto, A.; Garbo, S.; Karcz, T.; Lambona, C.; Marocco, F.; Latacz, G.; Sudoł-Tałaj, S.; Mordyl, B.; Głuch-Lutwin, M.; Siwek, A.; Czarnota-Łydka, K.; Gogola, D.; Olejarz-Maciej, A.; Wilczyńska-Zawal, N.; Honkisz-Orzechowska, E.; Starek, M.; Dąbrowska, M.; Kucwaj-Brysz, K.; Fioravanti, R.; Nasim, M. J.; Hittinger, M.; Partyka, A.; Wesołowska, A.; Battistelli, C.; Zwergel, C.; Handzlik, J. The first-in-class Se-containing potent 5-HT6 receptor agents with a beneficial neuroprotective profile against Alzheimer’s disease. J. Med. Chem. 2024, 67, 1580-1610.
  3. Kaczor, A.; Knutelska, J.; Kucwaj-Brysz, K.; Zygmunt, M.; Żesławska, E.; Siwek, A.; Bednarski, M.; Podlewska, S.; Jastrzębska-Więsek M.; Nitek, W.; Sapa, J.; Handzlik, J. The Subtype Selectivity in Search of Potent Hypotensive Agents among 5,5-Dimethylhydantoin Derived α 1 -Adrenoceptors Antagonists, Int. J. Mol. Sci. 2023, 24, 16609.
  4. Witek, K.; Kaczor, A.; Żesławska, E.; Podlewska, S.; Marć, M. A.; Czarnota-Łydka, K.; Nitek, W.; Latacz, G.; Tejchman, W.; Bischoff, M.; Jacob, C.; Handzlik, J. Chalcogen-varied imidazolone derivatives as antibiotic resistance breakers in Staphylococcus aureus strains. Antibiotics, 2023, 12, 1618.
  5. Wojtuch, A.; Danel, T.; Podlewska, S.; Maziarka, Ł. Extended Study on Atomic Featurization in GraphNeural Networks for Molecular Property Prediction. J. Cheminf. 2023, 15, 81.
  6. Sudoł-Tałaj, S.; Kucwaj-Brysz, K.; Podlewska, S.; Kurczab, R.; Satała, G.; Mordyl, B.; Głuch-Litwin, M.; Wilczyńska-Zawal, N.; Jastrzębska-Więcek, M.; Czarnota-Łydka, K.; Kurowska, K.; Kubacka, M.; Żesławska, E.; Nitek, W.; Olejarz-Maciej, A.; Doroz-Płonka, A.; Partyka, A.; Latacz, G.; Wesołowska, A.; Handzlik, J. Hydrophobicity modulation via the substituents at positions 2 and 4 of 1,3,5-triazine to enhance therapeutic ability against Alzheimer’s disease for potent serotonin 5-HT6R agents. Eur. J. Med. Chem. 2023, 260, 115756.
  7. Szczepańska, K.; Karcz, T.; Dichiara, M.; Mogilski, S.; Kalinowska-Tłuścik, J.; Pilarski, B.; Leniak, A.; Pietruś, W.; Podlewska, S.; Popiołek-Barczyk, K.; Humphrys, L. J.; Ruiz-Cantero, M. C.; Reiner-Link, D.; Leitzbach, L.; Łażewska, D.; Pockes, S.; Górka, M.; Zmysłowski, A.; Calmes, T.; Cobos, E. J.; Marrazzo, A.; Stark, H.; Bojarski, A. J.; Amata, E.; Kieć-Kononowicz, K. Dual piperidine-based histamine H3 and sigma-1 receptor ligands in the treatment of nociceptive and neuropathic pain. J. Med. Chem. 2023, 66, 9658-9683.
  8. Cieplinski, T.; Danel T.; Podlewska, S.; Jastrzębski, S. Generative models should at least be able to design molecules that dock well: a new benchmark. J. Chem. Inf. Model 2023, 63, 3238-3247.
  9. El Idrissi, I. G.; Podlewska, S.; Abate, C.; Bojarski, A. J.; Lacivita, E.; Leopoldo, M. Structure-Activity Relationships and Therapeutic Potential of Purinergic P2X7 Receptor Antagonists. Curr. Med. Chem. 2024, 31, 1361-1403.
  10. Duda, J.; Podlewska, S. Prediction of probability distributions of molecular properties – towards more efficient virtual screening and better understanding of compound representations. Mol. Divers. in press
  11. Danel, T.; Łęski, J.; Podlewska, S.; Podolak, I. T. Docking-based generative approaches in the search for new drug candidates. Drug Discov. Today, 2023, 28, 103439.
  12. Danel, T.; Wojtuch, A.; Podlewska, S. Generation of new inhibitors of selected cytochrome P450 subtypes– in silico study Comp. Struct. Biotechnol. J. 2022, 20, 5639-5651.
  13. Czarnota-Łydka, K.; Kucwaj-Brysz, K.; Pyka, P.; Haberek, W.; Podlewska, S.; Handzlik, J. Multitargeting the Action of 5-HT6 Serotonin Receptor Ligands by Additional Modulation of Kinases in the Search for a New Therapy for Alzheimer’s Disease: Can It Work from a Molecular Point of View? Int. J. Mol. Sci. 2022, 23, 8768.
  14. Baltrukevich, H.; Podlewska, S.; From data to knowledge:– systematic review of tools for automatic analysis of molecular dynamics output, Front. Pharmacol. 2022, 13, 844293.
  15. Penna, E.; Niso, M.; Podlewska, S.; Volpicelli, F.; Crispino, M. ; Perrone-Capano, C.; Bojarski, A.J.; Lacivita, E.; Leopoldo, M. In vitro and in silico analysis of the residence time of serotonin 5-HT7 receptor ligands with arylpiperazine structure: a structure-kinetics relationship study, ACS Chemical Neuroci. 2022, 13, 497-509.
  16. Kudla, L.; Bugno, R.; Podlewska, S.; Szumiec, L.; Wiktorowska, L.; Bojarski, A.J.; Przewlocki, R. Comparison of an addictive potential of μ-opioid receptor agonists with G protein bias: behavioral and molecular modeling studies, Pharmaceutics, 2022, 14, 55.
  17. Szczepańska, K.; Podlewska, S.; Dichiara, M.; Gentile, D.; Patamia, V.; Rosier, N.; Mönnich, D.; Ruiz-Cantero, M. C.; Karcz, T.; Łażewska, D.; Siwek, A.; Pockes, S.; Cobos, E. J.; Marrazzo, A.; Stark, H.; Rescifina, A.; Bojarski, A.J.; Amata, E.; Kiec-Kononowicz, K. Structural and molecular insight into piperazine and piperidine derivatives as histamine H3 and sigma-1 receptor antagonists with promising antinociceptive properties, ACS Chem. Neurosci. 2022, 13, 1-15.
  18. Kucwaj-Brysz, K.; Dela, A.; Podlewska, S.; Bednarski, M.; Siwek, A.; Satała, G.; Czarnota, K.; Handzlik, J.; Kieć-Kononowicz, K. The structural determinants for α1-adrenergic/serotonin receptors activity among phenylpiperazine-hydantoin derivatives. Molecules, 2021, 26, 7025.
  19. Wojtuch, A.; Jankowski, R.; Podlewska, S. How can SHAP values help to shape metabolic stability of chemical compounds? J. Cheminf., 2021, 13, 74.
  20. Głowacka, I. E.; Grabowska-Dużyc, M.; Andrei, G.; Schols, D.; Snoeck, R.; Witek, K.; Podlewska, S.; Handzlik, J.; Piotrowska, D. G. Novel N-substituted 3-aryl-4-(diethoxyphosphoryl)azetidin-2-ones as antibiotic enhancers and antiviral agents in search for a successful treatment of complex infections Int. J. Mol. Sci. 2021, 22, 8032.
  21. Podlewska, S..; Bugno, R.; Lacivita, E.; Leopoldo, M.; Bojarski, A.J.; Handzlik, J. Low Basicity as a Characteristic for Atypical Ligands of Serotonin Receptor 5-HT2 Int. J. Mol. Sci. 2021, 22, 1035.
  22. Podlewska, S.; Kurczab, R. Mutual Support of Ligand- and Structure-Based Approaches—To What ExtentWe Can Optimize the Power of Predictive Model? Case Study of Opioid Receptors  Molecules 2021, 26, 1607.
  23. Kaczor, A.; Witek, K.; Podlewska, S.; Sinou, V.; Czekajewska, J.; Żesławska, E.; Doroz-Płonka, A.; Lubelska, A.; Latacz, G.; Nitek, W.; Bischoff, M.; Alibert, S.; Pagès, J.-M.; Jacob, C.; Karczewska, E.; Bolla, J.-M.; Handzlik, J. Molecular Insights into an Antibiotic Enhancer Action of New Morpholine-Containing 5-Arylideneimidazolones in the Fight against MDR Bacteria. Int. J. Mol. Sci. 2021, 22, 2062.
  24. Minias, A.; Żukowska, L.; L echowicz, E.; Gąsior, F.; Knast, A.; Podlewska, S.; Dziadek, J. Early drug development and evaluation of putative antitubercular compounds in the -omics era. Front. Microbiol. 2021, 11, 618168.
  25. Szczepańska, K.; Pockes, S.; Podlewska, S.; Höring, C.; Mika, K.; Latacz, G.; Bednarski, M.; Siwek, A.; Karcz, T.; Nagl, M.; Bresinsky, M.; Mönnich, D.; Seibel, U.; Kuder, K. J.; Kotańska, M.; Stark, H.; Elz, S.; Kieć-Kononowicz, K. Structural modifications in the distal, regulatory region of histamine H3 receptor antagonists leading to the identification of a potent anti-obesity agent Eur. J. Med. Chem. 2021, 213, 113041.
  26. Kucwaj-Brysz, K.; Latacz, G.; Podlewska, S.; Żesławska, E.; Handzlik, J.; Lubelska, A.; Satała, G.; Nitek, W.; Handzlik, J. The relationship between stereochemical and both, pharmacological and ADME-Tox, properties of the potent hydantoin 5-HT7R antagonist MF-8 Bioorg. Chem. 2021, 106, 104466.
  27. Podlewska, S.; Bugno, R.; Kudla, L.; Bojarski, A. J.; Przewlocki, R. Molecular Modeling of μ Opioid Receptor Ligands with Various Functional Properties: PZM21, SR-17018, Morphine, and Fentanyl—Simulated Interaction Patterns Confronted with Experimental Data Molecules, 2020, 25, 4636.
  28. Rajda K.; Podlewska S. Similar, or dissimilar, that is the question. How different are methods for comparison of compounds similarity? Comput. Biol. Chem. 2020, 88, 107367.
  29. Jastrzębski, S.; Szymczak, M.; Pocha, A.; Mordalski, S.; Tabor, J.; Bojarski, A. J.; Podlewska S. Emulating Docking Results Using a Deep Neural Network: A New Perspective for Virtual Screening. J. Chem. Inf. Model. 2020, 60, 4246-4262
  30. Podlewska, S., Latacz, G.; Łażewska, D.; Kieć-Kononowicz, K.; Handzlik, J. In silico and in vitro studies on interaction of novel non-imidazole histamine H3R antagonists with CYP3A4. Bioorg. Med. Chem. Lett. 2020, 30, 127147.
  31. Sieradzki, I.; Lesniak, D.; Podlewska, S. How Sure Can We Be about ML Methods-Based Evaluation of Compound Activity: Incorporation of Information about Prediction Uncertainty Using Deep Learning Techniques. Molecules, 2020, 25, 1452.
  32. Leśniak, D.; Podlewska, S.; Jastrzębski, S.; Sieradzki, I.; Bojarski, A.J.; Tabor, J. Development of new methods needs proper evaluation – benchmarking sets for machine learning experiments for class A GPCRs, J. Chem. Inf. Model. 2019, 59, 4974-4992.
  33. Jastrzębski, S.; Sieradzki, I.; Leśniak, D.; Tabor, J.; Bojarski, A.J.; Podlewska, S. Three-dimensional descriptors for aminergic GPCRs: dependence on docking conformation and crystal structure, Mol. Divers. 2019, 23 (3), 603-613
  34. Kaczor, A.; Witek, K.; Podlewska, S.; Czekajewska, J.; Lubelska, A.; Żesławska, E.; Nitek, W.; Latacz, G.; Alibert, S.; Pages, J-M.; Karczewska, E.; Kieć-Kononowicz, K.; Handzlik, J.  5-Arylideneimidazolones with Amine at Position 3 as Potential Antibiotic Adjuvants against Multidrug Resistant Bacteria  Molecules 2019, 24(3), 438
  35. Vass, M.; Podlewska, S.; de Esch, I.J.P.; Bojarski, A. J.; Leurs, R.; Kooistra, A. J.; de Graaf, C. Aminergic GPCR–Ligand Interactions: A Chemical and Structural Map of Receptor Mutation Data, J. Med. Chem., 2019, 62, 3784-3839.
  36. Rataj K.; Czarnecki, W.; Podlewska, S.; Pocha, A.; Bojarski, A. J. Substructural Connectivity Fingerprint and Extreme Entropy Machines—A New Method of Compound Representation and Analysis. Molecules 2018, 23, 1242.
  37. Podlewska, S.; Kafel, R.; Lacivita, E.; Satała, G.; Kooistra, A. J.; Vass, M.; de Graaf, C.; Leopoldo, M.; Bojarski, A. J.; Mordalski, S. Structural insights into serotonin receptor ligands polypharmacology. Eur. J. Med. Chem., 2018, 151, 797-814.
  38. Podlewska, S.; Kafel, R. MetStabOn – online platform for metabolic stability predictions. Int. J. Mol. Sci. 2018, 19, 1040.
  39. Podlewska, S.; Czarnecki, W.; Kafel, R.; Bojarski, A.J. Creating the New from the Old: Combinatorial Libraries Generation with Machine-Learning-Based Compound Structure Optimization. J. Chem. Inf. Model. 2017, 57, 133-147.
  40. Leśniak, D.; Jastrzębski, S.; Podlewska, S.; Czarnecki, W. M.; Bojarski, A. J. Quo vadis G protein-coupled receptor ligands? A tool for analysis of the emergence of new groups of compounds over time. Bioorg Med Chem Lett. 2017, 27, 626-631 (https://www.ncbi.nlm.nih.gov/pubmed/27993519).
  41. Lacivita, E.; Podlewska, S.; Speranza, L.; Niso, M.; Satała, G.; Perrone, R.; Perrone-Capano, C.; Bojarski, A. J.; Leopoldo, M. Structural modifications of the serotonin 5-HT7 receptor agonist N-(4-cyanophenylmethyl)-4-(2-biphenyl)-1-piperazinehexanamide (LP-211) to improve in vitro microsomal stability: A case study, Eur. J. Med. Chem., 2016, 120, 363-379 (http://www.ncbi.nlm.nih.gov/pubmed/27318552)
  42. Kucwaj-Brysz, K.; Warszycki, D.; Podlewska, S.; Witek, J.; Witek, K.; González Izquierdo, A.; Satała, G.; Loza, M.I.; Lubelska, A.; Latz, G.; Bojarski, A.J.; Castro, M.; Kieć-Kononowicz, K.; Handzlik, J. Rational design in search for 5-phenylhydantoin selective 5-HT7R antagonists. Molecular modeling, synthesis and biological evaluation, Eur. J. Med. Chem., 2016, 112, 258-269 (http://www.ncbi.nlm.nih.gov/pubmed/26900658)
  43. Czarnecki, W.M.; Podlewska, S.; Bojarski, A.J. Extremely Randomized Machine Learning Methods for Compound Activity Prediction. Molecules, 2015, 11, 20107-20117 (http://www.ncbi.nlm.nih.gov/pubmed/26569196).
  44. Czarnecki, W. M.; Podlewska, S.; Bojarski, A. J. Robust optimization of SVM hyperparameters in the classification of bioactive compounds. J. Cheminform. 2015, 7:38, 1-15 (http://www.jcheminf.com/content/7/1/38)
  45. Czarnecki, W.M., Jastrzebski, S., Sieradzki, I., Podlewska, S. Active Learning of Compounds Activity – Towards Scientifically Sound Simulation of Drug Candidates Identification. Proceedings of 2nd Workshop on Machine Learning in Life Sciences, 2015, ISBN: 978-83-943803-0-4, 40–51 (http://issuu.com/paweksieniewicz/docs/mlls_2015_proceedings/3?e=6403122/31441344)
  46. Matys, A.; Podlewska, S.; Witek, K.; Witek, J.; Bojarski, A.J.; Schabikowski, J.; Otrębska-Machaj, E.; Latacz, G.; Szymańska, E.; Kieć-Kononowicz, K.; Molnar, J.; Amaral, L.; Handzlik, J. Imidazolidine-4-one derivatives in the search for novel chemosensitizers of Staphylococcus aureus MRSA: Synthesis, biological evaluation and molecular modeling studies. Eur. J. Med. Chem. 2015, 101, 313-325 (http://www.ncbi.nlm.nih.gov/pubmed/26160112)
  47. Smusz, S.; Kurczab, R.; Satała, G.; Bojarski, A. J. Fingerprint-based consensus virtual screening towards structurally new 5-HT6R ligands. Bioorg Med Chem Lett. 2015, 25, 1827-1830 (http://www.ncbi.nlm.nih.gov/pubmed/25866241)
  48. Smusz, S.; Mordalski, S.; Witek, J.; Rataj, K.; Kafel, R.; Bojarski, A.J.  Multi-Step Protocol for Automatic Evaluation of Docking Results Based on Machine Learning Methods-A Case Study of Serotonin Receptors 5-HT6 and 5-HT7. J. Chem. Inf. Model. 2015, 55, 823-832. (http://www.ncbi.nlm.nih.gov/pubmed/25806997)
  49. Mordalski, S.; Witek, J.; Smusz, S.; Rataj, K.; Bojarski, A.J. Multiple conformational states in retrospective virtual screening – homology models vs. crystal structures: beta-2 adrenergic receptor case study. J. Cheminform. 2015, 9, 7:13.  (http://www.ncbi.nlm.nih.gov/pubmed/25949744)
  50. Smusz, S.; Czarnecki, W. M.; Warszycki, D.; Bojarski, A. J. Exploiting uncertainty measures in compounds activity prediction using support vector machines. Bioorg. Med. Chem. Lett. 2015, 25, 100-105 (http://www.ncbi.nlm.nih.gov/pubmed/25466199).
  51. Witek, J.; Smusz, S.; Rataj, K.; Mordalski, S.; Bojarski, A. J. An application of machine learning methods to structural interaction fingerprints–a case study of kinase inhibitors. Bioorg. Med. Chem. Lett.  2014, 24, 580-585 (http://www.ncbi.nlm.nih.gov/pubmed/24374279)
  52. Kurczab, R.; Smusz, S. ; Bojarski, A. J. The influence of negative training set size on machine learning-based virtual screening. J. Cheminform. 2014, 6:32, 1-9 (http://www.ncbi.nlm.nih.gov/pubmed/24976867)
  53. Smusz, S.; Kurczab, R. ; Bojarski, A. J. A multidimensional analysis of machine learning methods performance in the classification of bioactive compounds. Chemometr. Intell. Lab. Syst. 2013, 128, 89-100  (http://www.sciencedirect.com/science/article/pii/S0169743913001561).
  54. Smusz, S.; Kurczab, R. ; Bojarski, A. J. The influence of the inactives subset generation on the performance of machine learning methods. J. Cheminform. 2013, 5, 17 (http://www.ncbi.nlm.nih.gov/pubmed/23561266)

Participation in research projects:

a) as principal investigator:

  • from 12.04.2016 – 'Searching for the new lead structures for 5-HT7 serotonin receptor ligands with increased metabolic stability’, financed by the grant HARMONIA, No 2015/18/M/NZ7/00377 More
  • 01.10.2015–30.09.2016 – Wykorzystanie metod uczenia maszynowego w zadaniach komputerowo wspomaganego projektowania leków, ETIUDA 3 scholarship (UMO-2015/16/T/NZ2/00058)
  • 13.02.2014 – 12.02.2016 – ’Development of a tool for automatic evaluation of complexes of ligands with serotonin receptors based on the application of machine learning methods’, financed by the grant PRELUDIUM, No 2013/09/N/NZ2/01917 More

b) as executor:

  • from 01.11.2013 ‒ EXtention of academia-based PLATFORM to antidepressant hits discovery (PLATFORMex), Nr Pol-Nor/198887/73/2013
  • from 01.07.2013 ‒ Innovative therapies for neurodegenerative and neurodevelopmental diseases based on mGlu receptor allosteric modulators – Allosterix, NCBiR 178469, 2013-2015
  • 17.05.2012 – 16.05.2014 'Application of SIFt profiles in virtual-screening’ financed by „Diamentowy Grant” of the Ministry of Science and Higher Education, No 0046/DIA/2012/41
  • 01.08.2012 – 31.07.2013 'The influence of molecular fingerprint bit density on chemical pattern recognition effectiveness’, financed by the grant PRELUDIUM, No 2011/03/N/NZ2/02478
  • from 01.07.2011 – Depression – Mechanisms – Therapy, co-financed by European Union from the European Fund of Regional Development (EFRD), WND-POIG.01.01.02-12-004/09-00, 2010-2013, www.de-me-ter.pl,
  • 01.07.2011 – 30.06.2013 Prokog. Antagonists of 5-HT6 receptor as advanced antipsychotic drugs with pro-cognitive properties, co-financed by European Union from the European Fund of Regional Development (EFRD), UDAPOIG.01.03.01-12-063/09-02, www.prokog.pl
  • 01.07.2011–31.03.2013 ModAll. Allosteric modulation – new strategy in pharmacotherapy. Identification of the psychotropic roperties of glutamatergic receptor ligands of group III, co-financed by European Union from the European Fund of Regional Development (EFRD) WND-POIG.01.03.01-12-100/08, www.modall.pl
  • 01.10.2010–31.06.2011 Creating an academia-based platform to discover substances acting on serotonergic or glutamatergic systems as potential new antidepressant or anxiolytic drugs. Project PNRF –103–AI-1/07, supported by a grant from Norway through the Norwegian Financial Mechanism within the Polish-Norwegian Research Fund, www.cns-platform.eu

Scientific reports:

a) posters:

  1. Lacivita, E.; Podlewska, S.; Speranza, L.; Niso, M.; Satała, G.; Perrone, R.; Perrone-Capano, C.; Bojarski, A.J.; Leopoldo, M.Towards new 5-HT7R ligands with improved metabolic stability – synthesis of LP-211 derivatives and their comprehensive evaluation in silico and in vitro; The 2nd Central European Biomedical Congress „From emerging biochemical strategies to personalized medicine”, 15-18.06.2016, Kraków, Poland, Book of Abstracts, p. 122 Abstract Poster
  2. Podlewska, S.; Vass, M.; Kooistra, A.J.; Kafel, R.; Bojarski, A.J.; de Graaf, C. Aminergic GPCRs from a site-directed mutagenesis perspective – analysis and prediction; The 2nd Central European Biomedical Congress „From emerging biochemical strategies to personalized medicine”, 15-18.06.2016, Kraków, Poland, Book of Abstracts, p. 123 Abstract Poster
  3. Podlewska, S.; Czarnecki, W.M.; Kafel, R.; Bojarski, A.J. Teaching an old dog new tricks – optimized fingerprint as a basis for new compounds formation; GLISTEN Erlangen 2016 Conference, 06-08.04.2016, Erlangen, Germany, Book of Abstracts, P-62
  4. Mordalski,S.; Witek. J.; Podlewska, S.; Rataj, K.; Bojarski, A.J. Multiple conformational states in retrospective virtual screening – homology models vs. crystal structures. Beta-2 adrenergic receptor case study; GLISTEN Amsterdam 2015 Conference, 12-13.10.2015, Amsterdam, the Netherlands, Book of Abstracts, P08 Abstract Poster
  5. Podlewska, S.; Warszycki, D.; Bojarski, A.J. Because two is always better than one – towards the search of dual 5-HTx-SERT ligands; GLISTEN Amsterdam 2015 Conference, 12-13.10.2015, Amsterdam, the Netherlands, Book of Abstracts, P36 Abstract Poster
  6. Rataj, K.; Czarnecki, W.; Podlewska, S.; Bojarski, A.J. Structural connectivity fingerprints – a new way to represent and classify compounds; GLISTEN Amsterdam 2015 Conference, 12-13.10.2015, Amsterdam, the Netherlands, Book of Abstracts, P50 Abstract Poster
  7. Rataj, K.; Czarnecki, W.; Podlewska, S.; Bojarski, A.J. Structural connectivity fingerprints – a new method of compound representation; VII Conversatory on Medicinal Chemistry, 17-19.09.2015, Lublin, Poland, Book of Abstracts, P83 Abstract Poster
  8. Podlewska, S.; Lacivita, E.; Leopoldo, M.; Bojarski, A.J. Tools for in silico evaluation of cytochrome P450-mediated compounds metabolism, V Meeting of the Paul Ehrlich MedChem Euro-PhD Network, 03-05.07.2015, Kraków, Book of Abstracts, p. 85 Abstract
  9. Podlewska, S.; Lacivita, E.; Leopoldo, M.; Bojarski, A.J.  Narzędzia do oceny stabilności metabolicznej in silico. V Konferencja Doktorantów Uniwersytetu Jagiellońskiego – Collegium Medicum, 25-26.05.2015, Kraków, Poland, Zeszyty Naukowe Towarzystwa Doktorantów Uniwersytetu Jagiellońskiego, Numer 10 (1/2015), Bartłomiej Jałocha, Ed., e-ISSN 2082-3827, p-ISSN 2084-977X, str. 112. Abstract
  10. Smusz, S.; Witek, J.; Rataj. K; Mordalski, S.; Bojarski, A.J.; Structural interaction profiles combination as a method for optimization of its application in docking results analysis – beta-2 adrenergic receptor case study; The GLISTEN Budapest 2014 Conference, 02-04.10.2014, Budapest, Hungary, Book of Abstracts, P412 Abstract Poster
  11. Lacivita, E.; Niso, M.; Smusz, S.; Satała, G.; Bojarski, A.J.; Leopoldo, M.; Novel potent serotonin 5-HT7 receptor ligands: structural modification to improve pharmacokinetic properties and in silico prediction model; The GLISTEN Budapest 2014 Conference, 02-04.10.2014, Budapest, Hungary, Book of Abstracts, P408 Abstract
  12. Kucwaj-Brysz, K.;Warszycki, D.; Witek, J.; Smusz, S.; Satała, G.; Bojarski, A.J.; Handzlik, J.; Kieć-Kononowicz, K.; Hydantoin derivatives as selective serotonin 5-HT7 receptor ligands; The GLISTEN Budapest 2014 Conference, 02-04.10.2014, Budapest, Hungary, Book of Abstracts, P416 Abstract
  13. Rataj, K.; Czarnecki, W.; Smusz, S.; Bojarski, A.J.; 2-Dimensional substructural fingerprints – a novel method of compound structure representation; VIth Conversatory on Medicinal Chemistry, 18-20.09.2014, Lublin, Poland, Book of Abstracts, p. 148 Abstract Poster
  14. Witek, J.; Smusz, S.; Rataj, K.; Mordalski, S.; Bojarski, A.J.; Combination of structural interaction profiles as a method for optimization of its application in docking results analysis; VIth Conversatory on Medicinal Chemistry, 18-20.09.2014, Lublin, Poland, Book of Abstracts, p. 157 Abstract Poster
  15. Kucwaj-Brysz, K.; Warszycki, D.; Smusz, S.; Witek, J.; Satała, G.; Bojarski, A.J.; Handzlik, J.; Kieć-Kononowicz, K.; The structure-selectivity relationship studies for hydantoin-derived 5-HT7R ligands; VIth Conversatory on Medicinal Chemistry, 18-20.09.2014, Lublin, Poland, Book of Abstracts, p. 103 Abstract
  16. Lacivita, E.; Niso, M.; Smusz, S.; Satała, G.; Perrone, R.; Bojarski, A.J.; Leopoldo, M.; Towards metabolically stable serotonin 5-HT7 receptor ligands: structural modification of LP-211 and in silico prediction model, XXV Congresso Nazionale della Societa Chimica Italiana, 07-12.09.2014, Arcavacata di Rende, Italy, Book of Abstracts, p. 359 Abstract
  17. Smusz, S.; Mordalski, S.; Witek, J.; Rataj, K.; Bojarski, A.J.; A machine learning-based for docking results analysis; The 10th International Conference on Chemical Structures, 1-5.06.2014, Noordwijkerhout, the Netherlands, Book of Abstracts, p.143 Abstract
  18. Witek, J.; Smusz, S.; Rataj, K.; Mordalski, S.; Bojarski, A.J.; An application of ligand interaction profiles as a novel approach in virtual screening of GPCR ligands; Barcelona GPCR Spring Conference 2014, 28-30.04.2014, Barcelona, Spain, , Book of Abstracts, p. 26 Poster
  19. Smusz, S.; Czarnecki, W.; Warszycki, D.; Bojarski, A.J.; Uncertainty of the in vitro experiments in the predictive models construction; Barcelona GPCR Spring Conference 2014, 28-30.04.2014, Barcelona, Spain, Book of Abstracts, p. 32 Poster
  20. Rataj, K.; Czarnecki, W.; Smusz, S.; Bojarski, A.J.; 2D fingerprints – a new way of chemical structure description; Barcelona GPCR Spring Conference 2014, 28-30.04.2014, Barcelona, Spain, Book of Abstracts, p. 31 Poster
  21. Kurczab, R.; Smusz, S.; Zastosowanie metod chem- i bioinformatycznych w nowoczesnym projektowaniu nowych leków; I Ogólnopolskie Sympozjum Interdyscyplinarne Inter-Mix 2013, 21-24.03.2013, Pułtusk, Poland, Book of Abstracts, p.34. Poster
  22. Kujawski, J.; Popielarska, H.; Drabińska, B.; Myka, A.; Smusz, S.; Kurczab, R.; Bojarski, A.; Synthesis and complexation abilities of new indazole derivatives with expected cytostatic activity; 15th JCF-Fruhjahrssymposium, 06-09.03.2013, Berlin, Germany, Book of Abstracts, p.238.
  23. Smusz, S.; Kurczab, R.; Bojarski, A.J.; The insight on molecular fingerprint nature – how to enhance the virtual screening performance?; VIIIth Joint Meeting on Medicinal Chemistry, 30.06-04.07.2013, Lublin, Poland, Book of Abstracts, p.P-46. Abstract Poster
  24. Smusz, S.; Mordalski, S.; Witek, J.; Rataj, K.; Bojarski, A.J.; Automatic evaluation of complexes of ligands with serotonin receptors based on the application of machine learning methods; GPCR-Ligand Interactions, Structures, and Transmembrane Signalling: a European Research Network, 07-09.10.2013, Warsaw, Poland, Poster
  25. Witek, J.; Smusz, S.; Rataj, K.; Mordalski, S.; Bojarski, A.J.; An application of machine learning methods to Structural Interaction Fingerprints as a novel approach in the search for biologically active compounds; GPCR-Ligand Interactions, Structures, and Transmembrane Signalling: a European Research Network, 07-09.10.2013, Warsaw, Poland, Poster
  26. Mordalski, S.; Witek, J.; Rataj, K.; Smusz, S.; Bojarski, A.J.; Automated docking restrains assignment based on interaction profiles; GPCR Workshop 2013, 01-05.12.2013, Maui, Hawaii, USA, Book of Abstracts, p.54. Poster
  27. Smusz, S.; Mordalski, S.; Witek, J.; Rataj, K.; Bojarski, A.J.; A novel machine learning-based protocol for predicting biological activity of chemical compounds; GPCR Workshop 2013, 01-05.12.2013, Maui, Hawaii, USA, Book of Abstracts, p.58. Abstract Poster
  28. Smusz, S.; Kurczab, R.; Bojarski, A.J.; Composition of the set of inactives and the performance in the classification of bioactive compounds by machine learning methods; 5th Symposium of the Polish Bioinformatics Society, 25-27.05.2012, Gdańsk, Poland, Book of Abstracts, p. Talk3. Abstract Poster
  29. Mordalski, S.; Witek, J.; Rataj, K.; Smusz, S.; Bojarski, A.J.; A SIFt-guided approach to docking restrains assignment. An application to Virtual Screening;  Book of Abstracts, 5th Symposium of the Polish Bioinformatics Society, 25-27.05.2012, Gdańsk, Poland, p.Talk10. Abstract Poster
  30. Mordalski, S.; Smusz, S.; Esmaielbeiki, R.; Bojarski, A.J.; Feature selection for structure based pharmacophore model by means of Structural Interaction Fingerprint and 3D motif;  The 5th Conversatory of Medicinal Chemistry, 13-15.09.2012, Lublin, Poland, Book of Abstracts, p.P-95. Abstract Poster
  31. Smusz, S.; Kurczab, R. ; Bojarski, A.J.; Machine Learning Method as a Tool for Searching New 5-HT6 Ligands in Fingerprint-Based Consensus Experiment; The 5th Conversatory of Medicinal Chemistry, 13-15.09.2012, Lublin, Poland, Book of Abstracts, p.P-65. Abstract Poster
  32. Rataj, K.; Witek, J.; Mordalski, S.; Kristiansen, K.; Smusz, S.; Bojarski, A.J.; Mutation Mining: Automated Extraction of Mutation Data from Scientific Publications; The 5th Conversatory of Medicinal Chemistry, 13-15.09.2012, Lublin, Poland, Book of Abstracts, p.P-59. Abstract Poster
  33. Smusz, S.; Kurczab., R; Bojarski, A.J. The influence of hashed fingerprints density on the machine learning methods performance, The 8. German Conference on Cheminformatics, 11-13.11.2012, Goslar, Niemcy, Book of Abstracts: P-27. Abstract Poster
  34. Witek, J.; Rataj, K.; Smusz, S.; Mordalski, S.; Kosciolek, T. Bojarski A.J. Application of Structural Interaction Fingerprints into post-docking analysis – insight into activity and selectivity, The 8. German Conference on Cheminformatics, 11-13.11.2012, Goslar, Niemcy, Book of Abstracts: P-30. Abstract Poster
  35. Kurczab, R.; Smusz, S.; Bojarski A.J. The influence of training actives/inactives ratio on machine learning performance, The 8. German Conference on Cheminformatics, 11-13.11.2012, Goslar, Niemcy, Book of Abstracts: P-32. Abstract Poster
  36. Smusz, S.; Kurczab, R.; Warszycki, D.; Kościółek, T.; Mordalski, S.; Bojarski, A.J.; Hybridization of ligands as a way of generating combinatorial libraries of drug candidates; Spring Congress of Polish Chemical Society Student Section, 13-17.04.2011, Murzasichle, Poland, Book of Abstracts, p.130. Abstract Poster
  37. Smusz, S.; Kurczab, R. ; Bojarski, A.J.; Meta-Learning as an Improvement of Machine Learning Methods Performance in Virtual Screening; The 4th Conversatory of Medicinal Chemistry, 08-10.09.2011, Lublin, Poland, Book of Abstracts, p.P-57.Abstract Poster
  38. Kurczab, R.; Smusz, S. ; Bojarski, A.J.; Evaluation of different Machine Learning Methods for Ligand-based Virtual Screening; German Conference on Chemoinformatics, 07-09.11.2010, Goslar, Germany, Book of Abstracts, p.105. Abstract

b) speeches:

  1. Podlewska, S.; Kooistra, A.J.; Vass, M.; Kafel, R.; Bojarski, A.J.; de Graaf, C. Zdefiniowanie wymagań strukturalnych dla selektywności pomiędzy wybranymi podtypami receptorów serotoninowych a receptorem histaminowym H1; VI Konferencja Doktorantów Uniwersytetu Jagiellońskiego – Collegium Medicum, 23.04.2016, Kraków, Poland, Zeszyty Naukowe Towarzystwa Doktorantów Uniwersytetu Jagiellońskiego, Numer 12 (1/2016), Bartłomiej Jałocha, Wojciech Tomczyk, Eds., e-ISSN 2082-3827, p‑ISSN 2084-977X, str. 100 Abstract
  2. Podlewska, S.; Kurczab, R.; Satała, G.; Bojarski, A.J. New non-basic ligands of serotonin receptor 5-HT6 as a result of virtual screening based on machine-learning methods; VII Conversatory on Medicinal Chemistry, 17-19.09.2015, Lublin, Poland, Book of Abstracts, PP5 Abstract Poster
  3. Smusz, S.; Witek, J.; Matys, A.; Handzlik, J.; Bojarski, A.J; Kieć-Kononowicz, K.; Wyjaśnienie mechanizmu znoszenia lekooporności przez pochodne 5-arylidenoimidazolonu za pomocą metod modelowania molekularnego, IV Konferencja Doktorantów Uniwersytetu Jagiellońskiego – Collegium Medicum, 29-30.05.2014, Kraków, Poland, Zeszyty Naukowe Towarzystwa Doktorantów Uniwersytetu Jagiellońskiego, Numer 8 (1/2014), Anna Bogdala, Bartłomiej Jałocha, Ed.,  e-ISSN 2082-3827, p-ISSN 2084-977X, str. 30 Abstract
  4. Smusz, S.; Protokół do automatycznej oceny wyników dokowania oparty o metody uczenia maszynowego; okonania Naukowe Doktorantów II Edycja, 12.04.2014, Kraków, Poland, Book of Abstracts, p. 227 (ISBN 978-83-63058-40-1) Abstract
  5. Smusz, S.; Czarnecki, W.; Bojarski, A.J.; The influence of the SVM metaparameters selection on the compounds’ activity prediction, Seminar of the Group of Machine Learning Research JU, 13.03.2014, Krakow, Poland
  6. Smusz, S.; Kurczab, R.; Bojarski, A.J.; Composition of the set of inactives and the performance in the classification of bioactive compounds by machine learning methods; 5th Symposium of the Polish Bioinformatics Society, 25-27.05.2012, Gdańsk, Poland, Book of Abstracts, p. Talk3. Abstract Poster
  7. Smusz, S.; Kurczab, R. ; Bojarski, A.J.; Machine learning methods as virtual screening tools in computer-aided drug design; XXXIII Ogólnopolska Szkoła Chemii, 10-14.11.2010, Jastrzębia Góra, Poland, Book of Abstracts, p.35. Abstract

Experience gained in Poland:

  • 06.07–31.07.2009 – Central Laboratory for Measurement and Research, Jastrzębie-Zdrój, student practice

Experience gained in abroad:

  • 02.11.2015–01.03.2016 – University of Amsterdam (the Netherlands), Faculty of Chemistry, internship within the ETIUDA 3 scholarship
  • 09–16.07.2015 – University of Bari (Italy), Faculty of Pharmacy, research trip
  • 14–20.06.2015 – University of Amsterdam (the Netherlands), Faculty of Chemistry, research trip
  • 23–27.02.2015 – University of Amsterdam (the Netherlands), Faculty of Chemistry, research trip
  • 09–13.02.2015 – ETH Zürich (Switzerland), Department of Chemistry and Applied Biosciences, training visit
  • 17–25.11.2014 – University of Bari (Italy), Faculty of Pharmacy, research trip
  • 12–21.09.2011 – University of Tromso (Norway), Faculty of Health Science, Department of Medicinal Biology, research trip