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Journal articles
  • Chen, Ya; Stork, Conrad; Hirte, Steffen; Kirchmair, Johannes. 2019. NP-scout: Machine learning approach for the quantification and visualization of the natural product-likeness of small molecules. Biomolecules. 9: 1-17. doi: 10.3390/biom9020043
  • Ehm, Patrick; Lange, Faabiola; Hentschel, Carolin; Jepsen, Anneke; Glück, Madeleine; Nelson, Nina; Bettin, Bettina; de Bruyn Kops, Christina; Kirchmair, Johannes; Nalaskowski, Marcus; Jücker, Manfred. 2019. Analysis of the FLVR motif of SHIP1 and its importance for the protein stability of SH2 containing signaling proteins. Cellular Signalling. 63: 109380. doi: 10.1016/j.cellsig.2019.109380
  • Fan, Ningning; Bauer, Christoph; Stork, Conrad; de Bruyn Kops, Christina; Kirchmair, Johannes. 2019. ALADDIN: Docking Approach Augmented by Machine Learning for Protein Structure Selection Yields Superior Virtual Screening Performance. Molecular informatics. 38.
  • Friedrich, Nils-Ole; Flachsenberg, Florian; Meyder, Agnes; Sommer, Kai; Kirchmair, Johannes; Rarey, Matthias. 2019. Conformator: A Novel Method for the Generation of Conformer Ensembles. Journal of Chemical Information and Modeling. 59: 731-742. doi: 10.1021/acs.jcim.8b00704
  • Galster, Magdalena; Loeppenberg, Marius; Galla, Fabian; Börgel, Frederik; Agoglitta, Oriana; Kirchmair, Johannes; Holl, Ralph. 2019. Phenylethylene glycol-derived LpxC inhibitors with diverse Zn2+-binding groups. Tetrahedron. 75: 486-509. doi: 10.1016/j.tet.2018.12.011
  • Langeder, Julia; Grienke, Ulrike; Chen, Ya; Kirchmair, Johannes; Rollinger, Judith. 2019. Natural products against acute respiratory infections: Strategies and lessons learned. Journal of Ethnopharmacology. doi: 10.1016/j.jep.2019.112298
  • Mathai, Neann; Chen, Ya; Kirchmair, Johannes. 2019. Validation strategies for target prediction methods. Briefings in Bioinformatics. doi: 10.1093/bib/bbz026
  • Sicho, Martin; Stork, Conrad; Mazzolari, Angelica; de Bruyn Kops, Christina; Pedretti, Alessandro; Testa, Bernard; Vistoli, Giulio; Svozil, Daniel; Kirchmair, Johannes. 2019. 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. 59: 3400-3412. doi: 10.1021/acs.jcim.9b00376
  • Stork, Conrad; Chen, Ya; Sicho, Martin; Kirchmair, Johannes. 2019. Hit Dexter 2.0: Machine-Learning Models for the Prediction of Frequent Hitters. Journal of Chemical Information and Modeling. 59: 1030-1043. doi: 10.1021/acs.jcim.8b00677
  • Stork, Conrad; Embruch, Gerd; Sicho, Martin; de Bruyn Kops, Christina; Chen, Ya; Svozil, Daniel; Kirchmair, Johannes. 2019. NERDD: A web portal providing access to in silico tools for drug discovery. Bioinformatics. doi: 10.1093/bioinformatics/btz695
  • Wald, Jiri; Pasin, Marion; Richter, Martina; Walther, Christin; Mathai, Neann; Kirchmair, Johannes; Makarov, Vadim A.; Goessweiner-Mohr, Nikolaus; Marlovits, Thomas C.; Zanella, Irene; Real-Hohn, Antonio; Verdaguer, Nuria; Blaas, Dieter; Schmidtke, Michaela. 2019. 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. 116: 19109-19115. doi: 10.1073/pnas.1904732116
  • Wilm, Anke; Stork, Conrad; Schepky, Andreas; Kühnl, Jochen; Kirchmair, Johannes. 2019. Skin Doctor: Machine learning models for skin sensitization prediction that provide estimates and indicators of prediction reliability. International Journal of Molecular Sciences. 20: 4833.
  • de Bruyn Kops, Christina; Stork, Conrad; Sicho, Martin; Kochev, Nikolay; Svozil, Daniel; Jeliazkova, Nina; Kirchmair, Johannes. 2019. GLORY: Generator of the structures of likely cytochrome P450 metabolites based on predicted sites of metabolism. Frontiers in Chemistry. 7: 402. doi: 10.3389/fchem.2019.00402/abstract
  • Drexel, Meinrad; Kirchmair, Johannes; Santos-Sierra, Sandra. 2018. INH14, a small‐molecule urea derivative, inhibits the IKKα/β‐dependent TLR inflammatory response. ChemBioChem. 1-8. doi: 10.1002/cbic.201800647
  • Tyzack, Jonathan; Kirchmair, Johannes. 2018. Computational methods and tools to predict cytochrome P450 metabolism for drug discovery. Chemical Biology and Drug Design. 93: 377-386. doi: 10.1111/cbdd.13445
  • Wilm, Anke; Kühnl, Jochen; Kirchmair, Johannes. 2018. Computational approaches for skin sensitization prediction. Critical Reviews in Toxicology. 48: 738-760. doi: 10.1080/10408444.2018.1528207
Book sections
  • Chen, Ya; de Bruyn Kops, Christina; Kirchmair, Johannes. 2019. Resources for chemical, biological, and structural data on natural products. Chapter 2. In:
    • Kinghorn, A. Douglas; Falk, Heinz; Gibbons, Simon; Kobayashi, Jun’ichi; Asakawa, Yoshinori; Liu, Ji-Kai. 2019. Progress in the Chemistry of Organic Natural Products Volume 110: Cheminformatics in Natural Product Research. Springer Nature. 278 pages. ISBN: 978-3-030-14631-3.

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