Publications

Google Scholar Profile

(H-index: 11, i10-index: 10, Citations: 271)

Research Gate Profile

(RG Impact: 31.59)

RESEARCH ARTICLES

Published

  1. Gorniak, A. K.; Pomastowski, P.; Railean-Plugaru, V.; Žuvela, P.; Wong, M. W.; Pauter, K.; Szultka-Młyńska, M.; Buszewski, B. The study of the molecular mechanism of Lactobacillus paracasei clumping via divalent metal ions by electrophoretic separation. J. Chromatogr. A 2021, in press.
  2. Wong, F. J.; Hao, Y.; Ming, W.; Žuvela, P.; Teh, P.; Shi, J.; Li, J. Methods to overcome limited labeled data sets in machine learning-based optical critical dimension metrology. Proceedings Volume 11611, Metrology, Inspection, and Process Control for Semiconductor Manufacturing XXXV, 2021, 116111P.
  3. Lovrić, M.; Pavlović, K.; Žuvela, P.; Spataru, A.; Lučić, B.; Kern, R.; Wong, M. W. Machine learning in prediction of intrinsic aqueous solubility of drug-like compounds: generalization, complexity or predictive ability? (preprint)
  4. Buszewski, B.; Žuvela, P.; Król-Górniak, A.; Railean-Plugaru, V.; Rogowska, A.; Wong, M. W.; Yi, M.; Rodzik, A.; Sprynskyy, M.; Pomastowski, P. Interactions of zinc aqua complexes with ovalbumin at the forefront of the Zn2+/ZnO-OVO hybrid complex formation mechanism. Appl. Surf. Sci. 2020, 542, 148641.
  5. Žuvela, P.; Lovrić, M.; Yousefian-Jazi, A.; Liu, J. J. Ensemble Learning Approaches to Data Imbalance and Competing Objectives in Design of an Industrial Machine Vision System. Ind. Eng. Res. Chem. 2020, 59, 4636-4645.
  6. Ser, C. T.; Žuvela, P.; Wong, M. W. Prediction of corrosion inhibition efficiency of pyridines and quinolines on an iron surface using machine learning-powered quantitative structure-property relationships. Appl. Surf. Sci. 2020, 512, 145612.
  7. Buszewski, B.; Žuvela, P.; Sagandykova, G.; Walczak-Skierska, J.; Pomastowski, P.; David, J.; Wong, M. W. Mechanistic Chromatographic Column Characterization for the Analysis of Flavonoids Using Quantitative Structure-Retention Relationships Based on Density Functional Theory. Int. J. Mol. Sci. 2020, 21, 2053.
  8. Ciura, K.; Fedorowicz, J.; Žuvela, P.; Lovrić, M.; Kapica, H.; Baranowski, P.; Sawicki, W.; Wong, M. W.; Sączewski, J. Affinity of Antifungal Isoxazolo [3, 4-b] pyridine-3 (1H)-Ones to Phospholipids in Immobilized Artificial Membrane (IAM) Chromatography. Molecules 2020, 25, 4835.
  9. Ciura, K.; Fedorowicz, J.; Andrić, F.; Žuvela, P.; Greber, K. E.; Baranowski, P.; Kawczak, P.; Nowakowska, J.; Bączek, T.; Sączewski, J. Lipophilicity Determination of Antifungal Isoxazolo[3,4-b]pyridin-3(1H)-ones and Their N1-Substituted Derivatives with Chromatographic and Computational Methods. Molecules 2020, 24, 4311.
  10. Žuvela, P.; Liu, J. J.; Wong, M. W.; Bączek, T. Prediction of Chromatographic Elution Order of Analytical Mixtures Based on Quantitative Structure-Retention Relationships and Multi-Objective Optimization, Molecules 2020, 25, 3085.
  11. Liu, J. J.; Alipuly, A.; Wong, M. W.; Bączek, T.; Žuvela P.* Quantitative structure-retention relationships with non-linear programming for prediction of chromatographic elution order. Int. J. Mol. Sci. 2019, 20, 3443. [PDF], [HTML]
  12. Žuvela, P.; Lin, K.; Shu, C.; Zheng, W.; Lim, C. M.; Huang, Z. Fiber-optic Raman Spectroscopy with Nature-inspired Genetic Algorithms Enhances Real-time In Vivo Detection And Diagnosis of Nasopharyngeal Carcinoma. Anal. Chem. 2019, 91, 8101-8108.
  13. Žuvela, P.; David, J.; Yang, X.; Wong, M. W.; Huang, D. Non-Linear Quantitative Structure-Activity Relationships Modelling, Mechanistic Study and In-Silico Design of Flavonoids as Potent Antioxidants. Int. J. Mol. Sci. 2019, 20, 2328. [PDF], [HTML]
  14. Žuvela, P.; Skoczylas, M.; Liu, J. J.; Bączek, T.; Kaliszan, R.; Wong, M. W.; Buszewski, B. Column selection and characterization systems in reversed-phase liquid chromatography. Chem. Rev. 2019, 119, 3674-3729.
  15. Žuvela, P.; Liu, J. J.; Yi, M.; Pomastowski, P.; Sagandykova, G.; Belka, M.; David, J.; Bączek, T.; Szafrański, K.; Žolnovska, B.; Sławiński, J.; Supuran, C. T.; Buszewski, B. Target-based drug discovery through inversion of quantitative structure-drug-property relationships and molecular simulation: CA IX-sulphonamide complexes. J. Enzyme Inhib. Med. Chem. 2018, 33, 1430-1443. [PDF], [HTML]
  16. Brigljević B.; Žuvela, P.; Liu, J. J.; Woo, H. C., Choi, J. H. Fully Automated Approach for Bio-crude Mixture Modelling Based on GC-MS and Elemental Analyses. Comput. Aided Chem. Eng. 2018, 44, 913-918.
  17. Brigljević B.; Žuvela, P.; Liu, J. J.; Woo, H. C., Choi, J. H. Development of an automated method for modelling of bio-crudes originating from biofuel production processes based on thermochemical conversion. Appl. Energy 2018, 215, 670-678.
  18. Žuvela P.; David J.; Wong. M.W. Interpretation of ANN-based QSAR models for prediction of antioxidant activity of flavonoids. J. Comput. Chem. 2018, 39, 953-963.
  19. Buszewski, B.; Walczak, J.; Žuvela, P.; Liu, J. J. Non-target analysis of phospholipid and sphingolipid species in egg yolk using liquid chromatography/triple quadrupole tandem mass spectrometry. J. Chromatogr. A 2017, 1487, 179–186.
  20. Žuvela, P.; Liu, J. J. On feature selection for supervised learning problems involving high-dimensional analytical information. RSC Adv. 2016, 6, 82801–82809.
  21. Pomastowski, P.; Sprynskyy, M.; Žuvela, P.; Rafińska, K.; Milanowski, M.; Liu, J. J.; Yi, M.; Buszewski, B. Silver-Lactoferrin Nanocomplexes as a Potent Antimicrobial Agent. J. Am. Chem. Soc. 2016, 138, 7899–7909.
  22. Žuvela, P.; Macur, K.; Liu, J. J.; Bączek, T. Exploiting non-linear relationships between retention time and molecular structure of peptides originating from proteomes and comparing three multivariate approaches. J. Pharm. Biomed. Anal. 2016, 127, 94–100.
  23. Žuvela, P.; Liu, J. J.; Macur, K.; Bączek, T. Molecular Descriptor Subset Selection in Theoretical Peptide Quantitative Structure–Retention Relationship Model Development Using Nature-Inspired Optimization Algorithms. Anal. Chem. 2015, 87, 9876–9883.
  24. Žuvela, P.; Liu, J. J.; Plenis, A.; Bączek, T. Assessment of column selection systems using Partial Least Squares. J. Chromatogr. A 2015, 1420, 74–82.
  25. Ukić, Š.; Novak, M.; Žuvela, P.; Avdalović, N.; Liu, Y.; Buszewski, B.; Bolanča, T. Development of Gradient Retention Model in Ion Chromatography. Part I: Conventional QSRR Approach. Chromatographia 2014, 77, 985–996.

Pending

  1. Lim, Z. J.; Žuvela, P.; Ukić, Š.; Novak Stankov, M.; Bolanča, T.; Lovrić, M.; Wong, M. W.; Buszewski, B. Gradient retention time modelling in ion chromatography through ensemble machine learning-powered quantitative structure-retention relationships. 2021, under review.
  2. Ulenberg, S.; Žuvela, P.; Lovrić, M.; Lučić, B.; Kern, R.; Liu, J. J.; Bączek, T. Machine learning methods for cross-column prediction of retention time in reversed-phase liquid chromatography. 2021, in preparation.
  3. Brigljević, B.; Žuvela, P.; Niaz, H.; Lim, H.; Liu, J. J.; Wong, M. W. Large-scale validation of a biocrude mixture modelling and opitmization method. 2021, in preparation.

PATENTS

Granted

  1. Liu, J. J.; Brigljević, B.; Žuvela, P. Method for simultaneous modeling and complexity reduction of bio-crudes for process simulation. KR102073856B1, 2020, patent granted.
  2. Liu, J. J.; Yi, M.; Žuvela, P. Methods for target-based drug screening through numerical inversion of quantitative structure-drug performance relationships and molecular dynamics simulations. KR-10-2017-0085981, 2019, patent granted.

Pending

  1. Liu, J. J.; Žuvela, P.; Bączek, T.; Alipuly, A. Method of predicting chromatographic elution order of compounds. US-167400243, 2020 US patent pending.
  2. Liu, J. J.; Žuvela, P.; Bączek, T.; Alipuly, A. Methods for prediction of chromatographic elution order of chemical compounds, KR-20200143551, 2020, KR patent pending.
  3. Liu, J. J.; Yi, M.; Žuvela, P. Method for screening of target-based drugs through numerical inversion of quantitative structure-(drug) performance relationships and molecular dynamics simulation. US-16628976, 2020, US patent pending.
  4. Liu, J. J.; Brigljevic, B.; Žuvela, P. Method of simultaneous modeling and complexity reduction of bio-crudes for process simulation. US-16123062, 2019, US patent pending.
  5. Liu, J. J.; Yi, M.; Žuvela, P. Method for screening new targeted drugs through numerical inversion of quantitative structure-performance relationship and molecular dynamics computer simulation. 2019, PCT patent pending.
  6. Liu, J. J.; Brigljevic, B.; Žuvela, P. Method of simultaneous modeling and complexity reduction of bio-crudes for process simulation, US-16123062, 2019, US patent pending.

CONFERENCE PRESENTATIONS

  1. Lovrić, M.; Žuvela, P.; Lučić, B.; Kern, R.; Liu, J. J.; Bączek, T. Machine learning methods for cross-column prediction of retention time in reversed-phase liquid chromatography (oral presentation). IAPC-8 Meeting. Split, Croatia, 2019.
  2. Alipuly, A.; Žuvela, P.; Liu, J. J. Prediction of chromatographic elution order of analytes from quantitative structure retention time relationship via quadratic programming (oral presentation). 123rd KCS General Meeting. Suwon, Korea, 2019
  3. David, J.; Žuvela, P.; Supuran, C. T.; Wong, M. W. The role of non-covalent interactions in design of potent and selective inhibitors of carbonic anhydrases (oral presentation). 11th International Conference on Carbonic Anhydrases. Bucharest, Romania, 2018.
  4. Alipuly, A.; Žuvela, P.; Liu, J. J.; Bączek, T. Prediction of elution order using mathematical optimization in QSRR modelling as a means of accurate characterization of complex protein mixtures (oral presentation). 122nd KCS General Meeting. Daegu, Korea, 2018
  5. Žuvela, P.; Skoczylas, M.; Liu, J. J.; Bączek, T.; Kaliszan, R.; Wong, M. W.; Buszewski, B. Column selection in liquid chromatography (oral presentation). 24th International Symposium on Separation Sciences. Jasná, Slovakia, 2018.
  6. Žuvela, P.; Alipuly, A.; Liu, J. J.; Macur, K.; Bączek, T.; Prediction of chromatographic elution order of simple analytical mixtures through multi-objective optimization (oral presentation). 24th International Symposium on Separation Sciences. Jasná, Slovakia, 2018.
  7. Liu, J. J.; Žuvela, P. Target-Based Drug Discovery Using Big Data Analytics and Computational Chemistry (oral presentation). 2018 AIChE Annual Meeting (Pharmaceutical Discovery, Development and Manufacturing Forum). Pittsburgh, US, 2018.
  8. Brigljević, B.; Žuvela, P.; Liu, J. J.; Choi, J. H.; Woo, H. C. Fully automated approach for bio-crude mixture modelling based on GC-MS and elemental analyses (oral presentation). Process Systems Engineering, PSE 2018 conference. San Diego, US, 2018.
  9. Brigljević, B.; Žuvela, P.; Liu, J. J.; Choi, J. H. Software development for optimization of complex organic mixtures from thermochemical decomposition processes (oral presentation). Korean Society of Clean Technology Fall Meeting. Yeosu, Korea, 2017.
  10. Žuvela, P.; Liu, J. J.; Yi, M.; Pomastowski, P.; Sagandykova, G.; Bączek, T.; Sławinski, J.; Wong, M. W.; Buszewski, B. Target-based drug discovery through inverse quantitative structure-activity-lipophilicity relationships and molecular simulation (oral presentation). Korean Chemical Society Fall Meeting. Gwangju, Korea, 2017.
  11. Žuvela, P.; Liu, J. J.; Phan, H.; Yi, M.; Kawczak, P.; Belka, M.; Sławinski, J.; Bączek, T. Prediction and design of novel antitumor pharmaceuticals using chemometrics and computational chemistry (keynote lecture). 22nd International Symposium on Separation Sciences. Toruń, Poland, 2016.
  12. Pomastowski, P.; Žuvela, P.; Sprynskyy, M.; Rafińska, K.; Liu, J. J.; Yi, M.; Buszewski, B. Study of metal-protein nanocomplexes (oral presentation). 22nd International Symposium on Separation Sciences. Toruń, Poland, 2016.
  13. Žuvela, P.; Liu, J. J. Feature selection for regression and classification in analytical chemistry (poster presentation). 22nd International Symposium on Separation Sciences. Toruń, Poland, 2016.
  14. Žuvela, P.; Phan, T. H. T.; Liu, J. J.; Yi, M.; Belka, M.; Bączek, T. Drug discovery through reverse quantitative structure-retention relationships and molecular simulation (oral presentation). Korean Institute of Chemical Engineers fall symposium. Daejeon, Korea, 2016.
  15. Žuvela, P.; Liu, J. J. Extracting chemical information in supervised learning through state-of-the-art variable selection techniques (oral presentation). Korean Institute of Chemical Engineers fall symposium. Daejeon, Korea, 2016.
  16. Žuvela, P.; Liu, J. J.; Macur, K.; Bączek, T. On nonlinear machine learning methods for Quantitative structure-retention relationships modeling in proteomics (oral presentation). Korean Institute of Chemical Engineers spring symposium. Busan, Korea, 2016.
  17. Žuvela, P.; Liu, J. J.; Kawczak, P.; Belka, M.; Sławinski, J.; Bączek, T. Distinguishing antitumor sulfonamide derivatives’ cis and trans isomers using Quantitative structure-retention relationships (poster presentation). 117th General Meeting of the Korean Chemical Society. Seoul, Korea, 2016.
  18. Žuvela, P.; Liu, J. J.; Macur, K.; Bączek, T. Nature-inspired variable selection in retention time prediction of peptides originating from Bacillus subtilis proteomes. (poster presentation) IUPAC 2015, 45th World Chemistry Congress. Busan, Korea, 2015.
  19. Žuvela, P.; Liu, J. J.; Macur, K.; Bączek, T. Molecular descriptor subset selection based on nature-inspired algorithms for QSRR model development in proteomics. (poster presentation) HPLC 2015 – 42nd International Symposium on High Performance Liquid Phase Separations & Related Techniques. Geneva, Switzerland, 2015.
  20. Žuvela, P.; Liu, J. J.; Plenis, A.; Bączek, T. Evaluation of correlation structure between a column selection method and RP-LC analysis selectivity towards alfuzosin, lamotrigine, and their impurities. (poster presentation) HPLC 2015 – 42nd International Symposium on High Performance Liquid Phase Separations & Related Techniques. Geneva, Switzerland, 2015.
  21. Žuvela, P.; Liu, J. J.; Macur, K.; Bączek, T. Dimensionality reduction in peptide Quantitative structure-retention relationships using global optimization algorithms (oral presentation). Korean Institute of Chemical Engineers fall symposium. Seoul, Korea, 2015.
  22. Žuvela, P.; Liu, J. J.; Macur, K.; Bączek, T. Predicting retention time from molecular structure in proteomics: machine learning approach (oral presentation). 116th General Meeting of the Korean Chemical Society. Daegu, Korea, 2015.
  23. Žuvela, P.; Liu, J. J.; Macur, K.; Bączek, T. Variable selection in peptide QSRR model development: Metaheuristic optimization algorithms approach (oral presentation). Korean Institute of Chemical Engineers spring symposium. Jeju, Korea, 2015.
  24. Žuvela, P.*; Liu, J. J.; Macur, K.; Bączek, T. Molecular descriptor subset selection based on nature-inspired optimization algorithms (oral presentation). Korean Institute of Chemical Engineers Busan-Gyeongnam meeting. Busan, Korea, 2014.
  25. Novak, M.; Žuvela, P.; Ukić, Š.; Kušić, H.; Bolanča, T. The use of structural information of molecules for the prediction of retention in ion chromatography (poster presentation). 10th meeting of young chemical engineers. Zagreb, Croatia, 2014.
  26. Žuvela, P.; Novak, M.; Ukić, Š.; Bolanča, T. Development of gradient retention model in ion chromatography. Part I: Conventional QSRR approach (poster presentation). 19th International Symposium on Separation Sciences, New Achievements in Chromatography. Poreč, Croatia, 2013.
  27. Žuvela, P.; Ukić, Š.; Bolanča, T.; Rogošić, M. Optimization of Chromatographic Analysis: Chemometric Approach. Monitoring i analiza wody. Chromatograficzne metody oznaczania substancji o charakterze jonowym. (poster presentation). Toruń, Poland, 2013.

*best oral presentation award