| Data resources for the computer-guided discovery of bioactive natural products Y Chen, C de Bruyn Kops, J Kirchmair Journal of chemical information and modeling 57 (9), 2099-2111, 2017 | 166 | 2017 |
| Benchmarking commercial conformer ensemble generators NO Friedrich, C de Bruyn Kops, F Flachsenberg, K Sommer, M Rarey, ... Journal of chemical information and modeling 57 (11), 2719-2728, 2017 | 102 | 2017 |
| GLORYx: Prediction of the Metabolites Resulting from Phase 1 and Phase 2 Biotransformations of Xenobiotics C de Bruyn Kops, M Šícho, A Mazzolari, J Kirchmair Chemical Research in Toxicology, 2020 | 78 | 2020 |
| FAME 3: predicting the sites of metabolism in synthetic compounds and natural products for phase 1 and phase 2 metabolic enzymes M Šícho, C Stork, A Mazzolari, C de Bruyn Kops, A Pedretti, B Testa, ... Journal of chemical information and modeling 59 (8), 3400-3412, 2019 | 76 | 2019 |
| High-quality dataset of protein-bound ligand conformations and its application to benchmarking conformer ensemble generators NO Friedrich, A Meyder, C de Bruyn Kops, K Sommer, F Flachsenberg, ... Journal of chemical information and modeling 57 (3), 529-539, 2017 | 76 | 2017 |
| GLORY: generator of the structures of likely cytochrome P450 metabolites based on predicted sites of metabolism C de Bruyn Kops, C Stork, M Šícho, N Kochev, D Svozil, N Jeliazkova, ... Frontiers in chemistry 7, 402, 2019 | 62 | 2019 |
| NERDD: a web portal providing access to in silico tools for drug discovery C Stork, G Embruch, M Šícho, C de Bruyn Kops, Y Chen, D Svozil, ... Bioinformatics 36 (4), 1291-1292, 2020 | 55 | 2020 |
| FAME 2: simple and effective machine learning model of cytochrome P450 regioselectivity M Šícho, C de Bruyn Kops, C Stork, D Svozil, J Kirchmair Journal of chemical information and modeling 57 (8), 1832-1846, 2017 | 52 | 2017 |
| Hit Dexter: A Machine‐Learning Model for the Prediction of Frequent Hitters C Stork, J Wagner, NO Friedrich, C de Bruyn Kops, M Šícho, J Kirchmair ChemMedChem 13 (6), 564-571, 2018 | 44 | 2018 |
| Skin Doctor CP: conformal prediction of the skin sensitization potential of small organic molecules A Wilm, U Norinder, MI Agea, C de Bruyn Kops, C Stork, J Kühnl, ... Chemical Research in Toxicology 34 (2), 330-344, 2020 | 14 | 2020 |
| Resources for chemical, biological, and structural data on natural products Y Chen, C de Bruyn Kops, J Kirchmair Progress in the Chemistry of Organic Natural Products 110: Cheminformatics …, 2019 | 10 | 2019 |
| Alignment-based prediction of sites of metabolism C de Bruyn Kops, NO Friedrich, J Kirchmair Journal of Chemical Information and Modeling 57 (6), 1258-1264, 2017 | 10 | 2017 |
| ALADDIN: Docking approach augmented by machine learning for protein structure selection yields superior virtual screening performance N Fan, CA Bauer, C Stork, C de Bruyn Kops, J Kirchmair Molecular Informatics 39 (4), 1900103, 2020 | 9 | 2020 |
| Analysis of the FLVR motif of SHIP1 and its importance for the protein stability of SH2 containing signaling proteins PAH Ehm, F Lange, C Hentschel, A Jepsen, M Glück, N Nelson, B Bettin, ... Cellular Signalling 63, 109380, 2019 | 8 | 2019 |
| CYPstrate: a set of machine learning models for the accurate classification of cytochrome P450 enzyme substrates and non-substrates M Holmer, C de Bruyn Kops, C Stork, J Kirchmair Molecules 26 (15), 4678, 2021 | 6 | 2021 |
| GLORY: generator of the structures of likely cytochrome P450 metabolites based on predicted sites of metabolism. Front. Chem. 7, 402 C de Bruyn Kops, C Stork, M Šícho, N Kochev, D Svozil, N Jeliazkova, ... | 6 | 2019 |
| Development of Computational Approaches for the Prediction of Regioselectivity and the Likely Products of Xenobiotic Metabolism C de Bruyn Kops Staats-und Universitätsbibliothek Hamburg Carl von Ossietzky, 2020 | | 2020 |
Johannes KirchmairUniversity of ViennaVerified email at univie.ac.at
