Departments

The Theory Department's research focuses on understanding complex chemical, physical, and biological processes using advanced theoretical and computational methods. The four laboratories combine computational chemistry and artificial intelligence with experimental techniques to comprehensively address modern challenges in life and materials sciences.

We develop and apply a broad range of computational methods, ranging from quantum chemical approaches and molecular dynamics simulation to multiscale modeling, virtual screening, and modern machine learning and artificial intelligence tools. These advanced approaches enable the precise study of dynamics and function of complex (bio)molecular systems, design and optimization of biologically active molecules, potential drug candidates, and advanced materials. 

1. KAŁKA, Andrzej J., NOVOTNÝ, Aleš, STARE, Jernej. Rescaling of point charges as a way to improve the simple-to-use electrostatic embedding scheme developed to explore enzyme activity with QM-oriented software. Journal of chemical information and modeling. 2025, 65, 16, 8653–8663, ilustr. ISSN 1549-960X. https://pubs.acs.org/doi/10.1021/acs.jcim.5c01235
    

2. STEVANOVIĆ, Kristina, HERLAH, Barbara, PAVLIN, Matic, PERDIH, Andrej. Asymmetric T-segment binding and gate dynamics govern the final stages of the type IIA topoisomerase catalytic cycle. International journal of biological macromolecules. 2025, 327, 1, 1-17. https://www.sciencedirect.com/science/article/pii/S0141813025077736

3. KOLARIČ, Anja, GERME, Thomas, HRAST RAMBAHER, Martina, STEVENSON, Clare E. M., LAWSON, David M., BURTON, Nicolas P., VÖRÖS, Judit, MAXWELL, Anthony, MINOVSKI, Nikola, ANDERLUH, Marko. Potent DNA gyrase inhibitors bind asymmetrically to their target using symmetrical bifurcated halogen bonds. Nature communications. 2021, 12150-1-150-13. https://www.nature.com/articles/s41467-020-20405-8
    
4. BORIŠEK, Jure, AUPIČ, Jana, MAGISTRATO, Alessandra. Third metal ion dictates the catalytic activity of the two-metal-ion pre-ribosomal RNA-processing machinery. Angewandte Chemie : international edition. 2024, 63, 44, 1-8. https://onlinelibrary.wiley.com/doi/10.1002/anie.202405819
    
5. PRAŠNIKAR, Eva, LJUBIČ, Martin, PERDIH, Andrej, BORIŠEK, Jure. Machine learning heralding a new development phase in molecular dynamics simulations. Artificial intelligence review. 2024, 57, 4, 1-36. https://link.springer.com/article/10.1007/s10462-024-10731-4
    

6. OGRIS, Iza, ZUPANČIČ, Barbara, SOSIČ, Izidor, MERZEL, Franci, GOLIČ GRDADOLNIK, Simona. Mechanistic insight into the dynamics of Mur ligase through a comprehensive timescale-specific approach. Communications chemistry. 2025, 8, 285. https://www.nature.com/articles/s42004-025-01675-z

7. HUBMAN, Anže, MERZEL, Franci. Determination of thermal conductivities in liquids by identifying heat transport in nonequilibrium MD simulations. Journal of molecular liquids. 2023, 370, 1-7. https://www.sciencedirect.com/science/article/pii/S0167732222024552 

8. NTARAKAS Nikolaos, LAH, Maša, SVENŠEK, Daniel, POTISK Tilen, PRAPROTNIK, Matej. Dissipative particle dynamics models of encapsulated microbubbles and nanoscale gas vesicles for biomedical ultrasound simulations. ACS Applied Nano Materials.2025, 8, 32, 16053-16070. https://pubs.acs.org/doi/10.1021/acsanm.5c02783
    

9. JUG, Matevž, SVENŠEK, Daniel, POTISK, Tilen, PRAPROTNIK, Matej. Learning macroscopic equations of motion from dissipative particle dynamics simulations of fluids. Computer Methods in Applied Mecahnics and Engineering. 2024, 432, 117379. https://www.sciencedirect.com/science/article/pii/S0045782524006340?via%3Dihub

10. COSTE, Amaury, SLEJKO, Ema, ZAVADLAV, Julija, PRAPROTNIK, Matej. Developing and implicit solvation machine learning model for molecular simulations of ionic media. Journal of Chemical Theory and Computation. 2024, 20, 411–420. https://pubs.acs.org/doi/10.1021/acs.jctc.3c00984