Eugen Hruška, Ph.D.

Department of Biophysics and Physical Chemistry, Faculty of Pharmacy, Charles University, Czech Republic

ORCID ID: 0000-0001-5679-8419
Google Scholar: Rq6m2UIAAAAJ
ResearcherID: AAY-5878-2020
Scopus Author ID: 57193802868
ResearchGate: Eugen-Hruska-3
Twitter: @HruskaEugen

Research focus

  • High-throughput simulation and explainable machine learning of drug-protein interactions

Research experience

  • since 2023 Academic Assistant (tenure track), 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


  • 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


Charles University starting grant PRIMUS24/MED/004 "Quantitative prediction of drug metabolism", 2024-2027, PI

Selected publications

Hruska, E., Zhao, L., & Liu, F. (2022). Ground truth explanation dataset for chemical property prediction on molecular graphs. Preprint.

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.

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.

Hruska, E., Balasubramanian, V., Lee, H., Jha, S., & Clementi, C. (2020). Extensible and scalable adaptive sampling on supercomputers. J. Chem. Theory Comput.

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.


© Charles University, Faculty of Pharmacy in Hradec Králové, Akademika Heyrovského 1203, 500 03 Hradec Králové, Czech Republic
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