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Daniela Schuster 0001
Person information
- affiliation: Paracelsus Medical University, Salzburg, Austria
Other persons with the same name
- Daniela Schuster 0002 — University of Konstanz, Germany
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2020 – today
- 2024
- [i2]Daniela Schuster:
Abstaining Machine Learning - Philosophical Considerations. CoRR abs/2409.00706 (2024) - 2023
- [j16]Veronika Temml
, Jakub Kollár, Theresa Schönleitner, Anna Höll
Combination of In Silico and In Vitro Screening to Identify Novel Glutamate Carboxypeptidase II Inhibitors. J. Chem. Inf. Model. 63(4): 1249-1259 (2023) - [j15]Mark James Horgan, Lukas Zell, Bianka Siewert
Identification of Novel β-Tubulin Inhibitors Using a Combined In Silico/In Vitro Approach. J. Chem. Inf. Model. 63(20): 6396-6411 (2023) - [c2]Daniela Schuster, Jan M. Broersen, Henry Prakken:
On Floating Conclusions. DEON 2023: 199-215 - 2022
- [i1]Daniela Schuster:
Forms and Norms of Indecision in Argumentation Theory. CoRR abs/2203.02207 (2022) - 2021
- [c1]Daniela Schuster:
Forms and Norms of Indecision in Argumentation Theory. DEON 2021: 394-413
2010 – 2019
- 2019
- [r1]Daniela Schuster
Fingerprints and Pharmacophores. Encyclopedia of Bioinformatics and Computational Biology (2) 2019: 619-627 - 2016
- [j14]Teresa Kaserer
Optimized Virtual Screening Workflow for the Identification of Novel G-Quadruplex Ligands. J. Chem. Inf. Model. 56(3): 484-500 (2016) - [j13]Birgit Waltenberger, Ulrike Garscha
Discovery of Potent Soluble Epoxide Hydrolase (sEH) Inhibitors by Pharmacophore-Based Virtual Screening. J. Chem. Inf. Model. 56(4): 747-762 (2016) - 2014
- [j12]Veronika Temml
Discovery of New Liver X Receptor Agonists by Pharmacophore Modeling and Shape-Based Virtual Screening. J. Chem. Inf. Model. 54(2): 367-371 (2014) - [j11]Yvonne Krautscheid, Carl Johann Åke Senning, Simone B. Sartori
Pharmacophore Modeling, Virtual Screening, and in Vitro Testing Reveal Haloperidol, Eprazinone, and Fenbutrazate as Neurokinin Receptors Ligands. J. Chem. Inf. Model. 54(6): 1747-1757 (2014) - [j10]Jadel M. Kratz
Experimentally Validated hERG Pharmacophore Models as Cardiotoxicity Prediction Tools. J. Chem. Inf. Model. 54(10): 2887-2901 (2014) - 2012
- [j9]Susanne von Grafenstein, Judit Mihaly-Bison, Gerhard Wolber
Identification of Novel Liver X Receptor Activators by Structure-Based Modeling. J. Chem. Inf. Model. 52(5): 1391-1400 (2012)
2000 – 2009
- 2009
- [j8]Johannes Kirchmair
How To Optimize Shape-Based Virtual Screening: Choosing the Right Query and Including Chemical Information. J. Chem. Inf. Model. 49(3): 678-692 (2009) - 2008
- [j7]Patrick Markt, Caroline McGoohan, Brian Walker, Johannes Kirchmair
Discovery of Novel Cathepsin S Inhibitors by Pharmacophore-Based Virtual High-Throughput Screening. J. Chem. Inf. Model. 48(8): 1693-1705 (2008) - 2007
- [j6]Patrick Markt, Daniela Schuster
Pharmacophore modeling and parallel screening for PPAR ligands. J. Comput. Aided Mol. Des. 21(10-11): 575-590 (2007) - [j5]Theodora M. Steindl, Daniela Schuster
Parallel Screening and Activity Profiling with HIV Protease Inhibitor Pharmacophore Models. J. Chem. Inf. Model. 47(2): 563-571 (2007) - 2006
- [j4]Theodora M. Steindl, Daniela Schuster
High-throughput structure-based pharmacophore modelling as a basis for successful parallel virtual screening. J. Comput. Aided Mol. Des. 20(12): 703-715 (2006) - [j3]Daniela Schuster
Pharmacophore Modeling and in Silico Screening for New P450 19 (Aromatase) Inhibitors. J. Chem. Inf. Model. 46(3): 1301-1311 (2006) - [j2]Theodora M. Steindl, Daniela Schuster
Parallel Screening: A Novel Concept in Pharmacophore Modeling and Virtual Screening. J. Chem. Inf. Model. 46(5): 2146-2157 (2006) - 2005
- [j1]Daniela Schuster
The Identification of Ligand Features Essential for PXR Activation by Pharmacophore Modeling. J. Chem. Inf. Model. 45(2): 431-439 (2005)
Coauthor Index
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