Rupp, Matthias, Dr.

ETH Zürich Dr. Matthias Rupp Inst. f. Pharmazeutische Wiss. HCI G 474.2 Wolfgang-Pauli-Str. 10 8093 Zuerich Phone: +41 44 633 74 39 Fax: +41 44 633 13 79 E-Mail:

Areas of interest

• Pattern recognition in the molecular sciences
• Kernel-based machine learning
• Virtual screening, quantitative structure-activity/property relationships
• Approximation of quantum mechanics calculations by machine learning

Publications

• Zachary D. Pozun, Katja Hansen, Daniel Sheppard, Matthias Rupp, Klaus-Robert Müller, Graeme Henkelman: Optimizing transition states via kernel-based machine learning, Journal of Chemical Physics, 136(17): 174101, American Institute of Physics, 2012.
• Markus Hartenfeller, Heiko Zettl, Miriam Walter, Matthias Rupp, Felix Reisen, Ewgenij Proschak, Sascha Weggen, Holger Stark, Gisbert Schneider: DOGS: Reaction-Driven De Novo Design of Bioactive Compounds, PLoS Computational Biology, 8(2): e1002380, Public Library of Science, 2012.
• Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller, O. Anatole von Lilienfeld: Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning, Physical Review Letters, 108(5): 058301, American Physical Society, 2012.
• Hansen K., Baehrens D., Schroeter T., Rupp M., Müller K.-R., Visual Interpretation of Kernel-Based Prediction Models, Molecular Informatics, in press, Wiley, 2011.
• Wang Q., Birod K., Angioni C., Grösch S., Geppert T., Schneider P., Rupp M., Schneider G., Spherical Harmonics Coefficients for Ligand-Based Virtual Screening of Cyclooxygenase Inhibitors, PLoS ONE, 6(7): e21554, Public Library of Science, 2011.
• Sushko I., Novotarskyi S., Körner R., Kumar Pandey A., Rupp M., Teetz W., Brandmaier S., Abdelaziz A., Prokopenko V. V., Tanchuk V. Y., Todeschini R., Varnek A., Marcou G., Ertl P., Potemkin V., Grishina M., Gasteiger J., Schwab C., Baskin I. I., Palyulin V. A., Radchenko E. V., Welsh W. J., Kholodovych V., Chekmarev D., Cherkasov A., Aires-de-Sousa J., Zhang Q.-Y., Bender A., Nigsch F., Patiny L., Williams A., Tkachenko V., Tetko I. V., Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information, Journal of Computer Aided Molecular Design, 25(6): 533–554, Springer, 2011.
• Rupp M., Körner R., Tetko I. V., Predicting the pKa of small molecules, Combinatorial Chemistry & High Throughput Screening, 14(5): 307–327, Bentham, 2011.
• Rupp M., Körner R., Tetko I., Estimation of acid dissociation constants using graph kernels, Molecular Informatics, 29(10): 731–740, Wiley, 2010.
• Hähnke V., Rupp M., Krier M., Rippmann F., Schneider G., Pharmacophore alignment search tool (PhAST): Influence of Canonical Atom Labeling on Similarity Searching, Journal of Computational Chemistry, 31(15): 2810–2826, Wiley, 2010.
• Rupp M., Schneider G., Graph kernels for molecular similarity, Molecular Informatics, 29(4): 266–273, Wiley, 2010.
• Steri R., Schneider P., Klenner A., Rupp M., Schubert-Zsilavecz M., Schneider G., Target profile prediction: Cross-activation of peroxisome proliferator-activated receptor (PPAR) and farnesoid X receptor (FXR), Molecular Informatics, 29(4): 287–292, Wiley, 2010.
• Steri R., Rupp M., Proschak E., Schroeter T., Zettl H., Hansen K., Schwarz O., Müller-Kuhrt L., Müller K.-R., Schneider G., Schubert-Zsilavecz M., Truxillic acid derivatives act as peroxisome proliferator-activated receptor γ activators, Bioorganic & Medicinal Chemistry Letters, 20(9): 2920–2923, Elsevier, 2010.
• Rupp M., Schroeter T., Steri R., Zettl H., Proschak E., Hansen K., Rau, O., Schwarz,O., Müller-Kuhrt L., Schubert-Zsilavecz M., Müller K.-R., Schneider G., From machine learning to natural product derivatives selectively activating transcription factor PPARγ, ChemMedChem, 5(2): 191–194, Wiley, 2010.
• Rupp M., Schneider P., Schneider G., Distance phenomena in high-dimensional chemical descriptor spaces: Consequences for similarity-based approaches, Journal of Computational Chemistry, 30(14): 2285–2296, Wiley, 2009.
• Proschak E., Rupp M., Derksen S., Schneider G., Shapelets: Possibilities and limitations of shape-based virtual screening, Journal of Computational Chemistry, 29(1): 108–114, Wiley, 2008.
• Rupp M., Proschak E., Schneider G., Kernel approach to molecular similarity based on iterative graph similarity, Journal of Chemical Information and Modeling, 47(6): 2280–2286, American Chemical Society, 2007.