Research focus
- High-throughput simulation and explainable machine learning of drug-protein interactions
Research experience
- since 2023 Assistant professor, Faculty of Pharmacy, Charles University, Czechia
- 2020-2022 Postdoctoral Fellow, Emory University, USA, Research topic: High-throughput simulation of explicit solvation at DFT accuracy and explainable machine learning of chemical properties, Advisor: Fang Liu
- 2014-2020 Graduate Research Assistant, Rice University, USA, Thesis title: Adaptive sampling of Conformational Dynamics, Advisor: Cecilia Clementi
Education
- 2014-2020 Ph.D., Physics, Rice University, USA
- 2011-2014 Bachelor, Biochemistry, University of Regensburg, Germany
- 2011-2012 Bachelor, Technical Physics, Ilmenau University of Technology, Germany
Grants
Charles University starting grant PRIMUS24/MED/004 "Quantitative prediction of drug metabolism", 2024-2027, PI
Selected publications
Chen, X., Sun, Y., Hruska, E., Dixit, V., Yang, J., He, Y., Wang, Y., & Liu, F. (2025). Detecting thermodynamic phase transition via explainable machine learning of photoemission spectroscopy. Newton, 1(3). doi.org/10.1016/j.newton.2025.100066
Grenda, P., Ogos, M., & Hruska, E. (2025). Automated structural refinement of docked complexes in cytochrome P450 using molecular dynamics. Preprint. doi.org/10.26434/chemrxiv-2025-mvv4k-v3
Suwała, D., & Hruska, E. (2024). The wins and failures of current docking methods tested on the flexible active site of cytochromes P450. Preprint. doi.org/10.26434/chemrxiv-2024-05299-v2
Chen, X., Li, P., Hruska, E., & Liu, F. (2023). ∆-machine learning for quantum chemistry prediction of solution-phase molecular properties at the ground and excited states. Phys. Chem. Chem. Phys., 25(19), 13417–13428. doi.org/10.1039/D3CP00506B
Hruska, E., Zhao, L., & Liu, F. (2022). Ground truth explanation dataset for chemical property prediction on molecular graphs. Preprint. doi.org/10.26434/chemrxiv-2022-96slq-v2
Quantum Chemistry in the Age of Machine Learning, 1st Edition, Elsevier, Chapter 6: Machine learning: An overview, Eugen Hruska, Fang Liu, Editor: Pavlo Dral, ISBN: 9780323900492
Hruska, E., Gale, A., Huang, X., & Liu, F. (2022). AutoSolvate : A Toolkit for Automating Quantum Chemistry Design and Discovery of Solvated Molecules. J. Chem. Phys. doi.org/10.1063/5.0084833
Hruska, E., Gale, A., & Liu, F. (2022). Bridging the experiment-calculation divide: Machine learning corrections to redox potential calculations in implicit and explicit solvent models. J. Chem. Theory Comput. doi.org/10.1021/acs.jctc.1c01040
Hruska, E., Balasubramanian, V., Lee, H., Jha, S., & Clementi, C. (2020). Extensible and scalable adaptive sampling on supercomputers. J. Chem. Theory Comput. doi.org/10.1021/acs.jctc.0c00991
Hruska, E., Abella, J. R., Nüske, F., Kavraki, L. E., & Clementi, C. (2018). Quantitative comparison of adaptive sampling methods for protein dynamics. J. Chem. Phys. doi.org/10.1063/1.5053582