Dr. Christian Permann, MSc.

Christian Permann is a postdoctoral researcher in the Cheminformatics Research Group at the University of Vienna. He finished his Bachelor’s and thereafter Master’s degree in Computer Science with a Scientific Computing focus at the University of Vienna. His expertise lies in the design and implementation of high-performance algorithms for scientific applications. For his Master’s thesis, he developed a tool for visual cluster analysis and simplified semi-supervised consensus clustering. After his Master’s studies, he worked with the “Research Platform Next Generation Macrocycles to Address Challenging Protein Interfaces” as part of his PhD which aimed to improve the state of the art in the domain of computer-aided drug discovery, with special emphasis on macrocyclic molecules. His current work is related to the scalability and generalizability of state of the art methods in chemoinformatics such that those can be used accurately for all kinds of datasets while utilizing the available hardware resources as efficiently as possible.

Contact
E-Mail: christian.permann@univie.ac.at
Room: 2E 354

Showing entries 1 - 7 out of 7
Mattelaer CA, Fino R, Permann C, Langer T, Jacoby E, Harvey JN. Application of Semiempirical Quantum Mechanical Methods To Accurately Estimate Ligand-Binding Structure in Biological Systems: Protein Kinase Case Study. Journal of Chemical Information and Modeling. 2026 Jan 26;66(2):1231-1240. doi: 10.1021/acs.jcim.5c02274

Doijen J, Xie J, Marsili S, Bains T, Mann MK, Abeywickrema P et al. A New Fragment-Based Pharmacophore Virtual Screening Workflow Identifies Potent Inhibitors of SARS-CoV-2 NSP13 Helicase. Journal of Computational Chemistry. 2025 Sept 5;46(23):e70201. doi: 10.1002/jcc.70201

Bause F, Permann C, Kriege NM. Approximating the Graph Edit Distance with Compact Neighborhood Representations. In Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2024. . Vol. 14945. Springer Cham. 2024. p. 300-318. (Lecture Notes in Computer Science, Vol. 14945). doi: 10.1007/978-3-031-70362-1_18

Seidel T, Permann C, Wieder O, Kohlbacher SM, Langer T. High-Quality Conformer Generation with CONFORGE: Algorithm and Performance Assessment. Journal of Chemical Information and Modeling. 2023 Sept 11;63(17):5549-5570. doi: 10.1021/acs.jcim.3c00563

Kohlbacher SM, Ibis G, Permann C, Bryant S, Langer T, Seidel T. A new set of KNIME nodes implementing the QPhAR algorithm. Molecular Informatics. 2023 May;42(5):2200245. Epub 2023 Mar 5. doi: 10.1002/minf.202200245

Permann C, Seidel T, Langer T. Greedy 3-point search (G3ps)—a novel algorithm for pharmacophore alignment. Molecules. 2021 Nov 27;26(23):7201. doi: 10.3390/molecules26237201

Showing entries 1 - 7 out of 7