Univ.-Prof. Mag. Dr. Johannes Kirchmair
Johannes Kirchmair
61 - 80 out of 142
Chen Y, Mathai N, Kirchmair J. Scope of 3D Shape-Based Approaches in Predicting the Macromolecular Targets of Structurally Complex Small Molecules Including Natural Products and Macrocyclic Ligands. Journal of Chemical Information and Modeling. 2020 Jun 22;60(6):2858-2875. doi: 10.1021/acs.jcim.0c00161

Mathai N, Chen Y, Kirchmair J. Validation strategies for target prediction methods. Briefings in bioinformatics. 2020 May 21;21(3):791-802. doi: 10.1093/bib/bbz026

Mathai N, Kirchmair J. Similarity-Based Methods and Machine Learning Approaches for Target Prediction in Early Drug Discovery: Performance and Scope. International Journal of Molecular Sciences. 2020 May;21(10):3585. doi: 10.3390/ijms21103585

Fan N, Bauer CA, Stork C, de Bruyn Kops C, Kirchmair J. ALADDIN: Docking Approach Augmented by Machine Learning for Protein Structure Selection Yields Superior Virtual Screening Performance. Molecular Informatics. 2020 Apr;39(4):1900103. doi: 10.1002/minf.201900103

Stork C, Embruch G, Šícho M, de Bruyn Kops C, Chen Y, Svozil D et al. NERDD: a web portal providing access to in silico tools for drug discovery. Bioinformatics. 2020 Feb 15;36(4):1291-1292. doi: 10.1093/bioinformatics/btz695

Xue W, Li X, Ma G, Zhang H, Chen Y, Kirchmair J et al. N-thiadiazole-4-hydroxy-2-quinolone-3-carboxamides bearing heteroaromatic rings as novel antibacterial agents: Design, synthesis, biological evaluation and target identification. European Journal of Medicinal Chemistry. 2020 Feb 15;188:112022. doi: 10.1016/j.ejmech.2019.112022

Langeder J, Grienke U, Chen Y, Kirchmair J, Schmidtke M, Rollinger JM. Natural products against acute respiratory infections: Strategies and lessons learned. Journal of Ethnopharmacology. 2020 Feb 10;248:112298. doi: 10.1016/j.jep.2019.112298

Ehm PAH, Lange F, Hentschel C, Jepsen A, Glück M, Nelson N et al. Analysis of the FLVR motif of SHIP1 and its importance for the protein stability of SH2 containing signaling proteins. Cellular signalling. 2019 Nov;63:109380. doi: 10.1016/j.cellsig.2019.109380

Wilm A, Stork C, Bauer C, Schepky A, Kühnl J, Kirchmair J. Skin Doctor: Machine Learning Models for Skin Sensitization Prediction that Provide Estimates and Indicators of Prediction Reliability. International Journal of Molecular Sciences. 2019 Oct;20(19):4833. doi: 10.3390/ijms20194833

Wald J, Pasin M, Richter M, Walther C, Mathai N, Kirchmair J et al. Cryo-EM structure of pleconaril-resistant rhinovirus-B5 complexed to the antiviral OBR-5-340 reveals unexpected binding site. Proceedings of the National Academy of Sciences of the United States of America (PNAS). 2019 Sept 17;116(38):19109-19115. doi: 10.1073/pnas.1904732116

Šícho M, Stork C, Mazzolari A, de Bruyn Kops C, Pedretti A, Testa B et al. FAME 3: Predicting the Sites of Metabolism in Synthetic Compounds and Natural Products for Phase 1 and Phase 2 Metabolic Enzymes. Journal of Chemical Information and Modeling. 2019 Aug 26;59(8):3400-3412. doi: 10.1021/acs.jcim.9b00376

Tyzack JD, Kirchmair J. Computational Methods and Tools to Predict Cytochrome P450 Metabolism for Drug Discovery. Chemical Biology and Drug Design. 2019 Apr;93(4):377-386. Epub 2018 Nov 24. doi: 10.1111/cbdd.13445

Drexel M, Kirchmair J, Santos-Sierra S. INH14, a small-molecule urea derivative, inhibits the IKKα/β-dependent TLR inflammatory response. ChemBioChem: a european journal of chemical biology. 2019 Mar 1;20(5):710-717. Epub 2018 Nov 17. doi: 10.1002/cbic.201800647

Friedrich NO, Flachsenberg F, Meyder A, Sommer K, Kirchmair J, Rarey M. Conformator: A Novel Method for the Generation of Conformer Ensembles. Journal of Chemical Information and Modeling. 2019 Feb 25;59(2):731-742. doi: 10.1021/acs.jcim.8b00704

Galster M, Löppenberg M, Galla F, Börgel F, Agoglitta O, Kirchmair J et al. Phenylethylene glycol-derived LpxC inhibitors with diverse Zn2+-binding groups. Tetrahedron. 2019 Jan 25;75(4):486-509. Epub 2018 Dec 10. doi: 10.1016/j.tet.2018.12.011


Kirchmair J. Editorial for the Special Section "Artificial Intelligence in Drug Discovery". Drug Discovery Today: Technologies. 2019;32-33:1-2. doi: 10.1016/j.ddtec.2020.12.001

de Bruyn Kops C, Stork C, Šícho M, Kochev N, Svozil D, Jeliazkova N et al. GLORY: Generator of the structures of likely cytochrome P450 metabolites based on predicted sites of metabolism. Frontiers in Chemistry. 2019;7:402. doi: 10.3389/fchem.2019.00402

Chen Y, de Bruyn Kops C, Kirchmair J. Resources for Chemical, Biological, and Structural Data on Natural Products. In Progress in the chemistry of organic natural products. Vol. 110. 2019. p. 37-71. (Progress in the Chemistry of Organic Natural Products). doi: 10.1007/978-3-030-14632-0_2

Wilm A, Kühnl J, Kirchmair J. Computational approaches for skin sensitization prediction. Critical Reviews in Toxicology. 2018 Nov 29;48(9):738-760. doi: 10.1080/10408444.2018.1528207

61 - 80 out of 142

Computational approaches for predicting assay interference

Kirchmair, J. (Speaker)

23 May 2024

Activity: Talks and presentationsTalk or oral contributionScience to Science


Computational methods for early drug discovery

Kirchmair, J. (Speaker)

5 Apr 2024

Activity: Talks and presentationsTalk or oral contributionScience to Science


Activity Cliffs Go Smooth: Graph Siamese Neural Networks for Molecular Activity Prediction

Mqawass, G. (Speaker), Hirte, S. (Speaker), Kirchmair, J. (Speaker) & Kriege, N. M. (Speaker)

8 Dec 2023

Activity: Talks and presentationsTalk or oral contributionScience to Science


Maximizing the performance of similarity-based virtual screening methods

Kirchmair, J. (Speaker)

19 Apr 2023

Activity: Talks and presentationsTalk or oral contributionScience to Science


In silico methods for flagging compounds that are likely to interfere with biological assays

Kirchmair, J. (Invited speaker)

16 Dec 2022

Activity: Talks and presentationsTalk or oral contributionScience to Science


Cheminformatics in natural product-based drug discovery

Kirchmair, J. (Invited speaker)

24 Nov 2022

Activity: Talks and presentationsTalk or oral contributionScience to Science


From in silico to in vivo - Psychotria nemorosa alkaloids counter protein toxicity in Caenorhabditis elegans

Kirchweger, B. (Speaker), Klein-Junior, L. C. (Contributor), Pretsch, D. (Contributor), Chen, Y. (Contributor), Cretton, S. (Contributor), de Gasper, A. L. (Contributor), Heyden, Y. V. (Contributor), Christen, P. (Contributor), Kirchmair, J. (Contributor), Henriques, A. T. (Contributor) & Rollinger, J. M. (Contributor)

31 Aug 2022

Activity: Talks and presentationsTalk or oral contributionScience to Science


Natural products against SARS-CoV-2 or how to catch a butterfly?

Wasilewicz, A. (Speaker), Kirchweger, B. (Contributor), Bojkova, D. (Contributor), Abi Saad, M. J. (Contributor), Langeder, J. (Contributor), Grienke, U. (Contributor), Cinatl, J. (Contributor), Orts, J. (Contributor), Kirchmair, J. (Contributor), Rabenau, H. F. (Contributor) & Rollinger, J. M. (Contributor)

31 Aug 2022

Activity: Talks and presentationsTalk or oral contributionScience to Science


Cheminformatics in Natural Product-Based Drug Discovery

Kirchmair, J. (Keynote speaker)

30 Aug 2022

Activity: Talks and presentationsTalk or oral contributionScience to Science


Computerbasierte Vorhersage des Metabolismus von Wirkstoffen

Kirchmair, J. (Invited speaker)

12 Jul 2022

Activity: Talks and presentationsTalk or oral contributionScience to Science


Cheminformatics in natural product-based drug discovery

Kirchmair, J. (Invited speaker)

30 Jun 2022

Activity: Talks and presentationsTalk or oral contributionScience to Science


Introduction to machine learning in early drug discovery

Kirchmair, J. (Invited speaker)

21 Jun 2022

Activity: Talks and presentationsTalk or oral contributionScience to Science


In silico and in vitro Approach to Assess Direct Allosteric AMPK Activators from Nature

Kirchweger, B. (Speaker), Wasilewicz, A. (Contributor), Fischhuber, K. (Contributor), Tahir, A. (Contributor), Chen, Y. (Contributor), Heiß, E. (Contributor), Langer, T. (Contributor), Kirchmair, J. (Contributor) & Rollinger, J. M. (Contributor)

20 May 2022

Activity: Talks and presentationsTalk or oral contributionScience to Science


In silico prediction of xenobiotic metabolism

Kirchmair, J. (Invited speaker)

19 May 2022

Activity: Talks and presentationsTalk or oral contributionScience to Science


In silico prediction of drug metabolism

Kirchmair, J. (Invited speaker)

4 May 2022

Activity: Talks and presentationsTalk or oral contributionScience to Science


Computational prediction of the metabolic fate of small molecules

Kirchmair, J. (Speaker)

28 Sept 2021

Activity: Talks and presentationsTalk or oral contributionScience to Science


Computational methods for flagging compounds likely to cause false outcomes in biological assays

Kirchmair, J. (Speaker)

7 Jun 2021

Activity: Talks and presentationsTalk or oral contributionScience to Science


In silico prediction of the macromolecular targets of natural products

Kirchmair, J. (Speaker)

9 Mar 2021

Activity: Talks and presentationsTalk or oral contributionScience to Science


Computational prediction of xenobiotic metabolism (focus on AI/machine learning methods)

Kirchmair, J. (Speaker)

27 Jan 2021

Activity: Talks and presentationsTalk or oral contributionScience to Science


Anti-Infectives Drug Discovery

Rollinger, J. M. (Project Lead), Dailey, L. A. (Co-Lead), Orts, J. (Co-Lead), Böttcher, T. (Co-Lead), Schmidt, M. (Co-Lead), Rademacher, C. J. H. (Co-Lead), Schützenmeister, N. (Co-Lead), Zotchev, S. (Co-Lead), Kirchmair, J. (Co-Lead) & Ortmayr, K. (Co-Lead)

1/07/2530/06/29

Project: Research funding


AIDD: Advanced Machine Learning for Innovative Drug Discovery

Kirchmair, J. (Project Lead)

1/01/2131/12/24

Project: Research funding


Department of Pharmaceutical Sciences

Josef-Holaubek-Platz 2 (UZA II)
1090 Wien
Room: 2G353

T: +43-1-4277-55104

johannes.kirchmair@univie.ac.at