A. Jeltsch, P. Schnee, M. Khella, S. Weirich, J. Pleiss, and P. Bashtrykov. Software, (2023)Related to: Mina S. Khella, Philipp Schnee, Sara Weirich, Tan Bui, Alexander Bröhm, Pavel Bashtrykov, Jürgen Pleiss, Albert Jeltsch: The T1150A cancer mutant of the protein lysine methyltransferase NSD2 can introduce H3K36 trimethylation. J Biol Chem, 2023, 5, 104796. doi: 10.1016/j.jbc.2023.104796.
M. Kirchhof. Dataset, (2021)Related to: M. Kirchhof, K. Gugeler, F. R. Fischer, M. Nowakowski, A. Bauer, S. Alvarez-Barcia, K. Abitaev, M. Schnierle, Y. Qawasmi, W. Frey, A. Baro, D. P. Estes, T. Sottmann, M. R. Ringenberg, B. Plietker, M. Bauer, J. Kästner, S. Laschat, Organometallics 2020, 39, 3131-3145. doi: 10.1021/acs.organomet.0c00310.
M. Deimling, and M. Kirchhof. Dataset, (2021)Related to: Asymmetric Catalysis in Liquid Confinement: Probing the Performance of Novel Chiral Rhodium-Diene Complexes in Microemulsions and Conventional Solvents. M. Deimling, M. Kirchhof, B. Schwager, Y. Qawasmi, A. Savin, T. Mühlhäuser, W. Frey, B. Claasen, A. Baro, T. Sottmann, S. Laschat, Chem. Eur. J. 2019, 25, 9464. doi: 10.1002/chem.201900947.
D. Holzmüller, V. Zaverkin, J. Kästner, and I. Steinwart. Software, (2022)Related to: David, Holzmüller, Viktor Zaverkin, Johannes Kästner, and Ingo Steinwart. A Framework and Benchmark for Deep Batch Active Learning for Regression, 2022. arXiv: 2203.09410.
D. Holzmüller, V. Zaverkin, J. Kästner, and I. Steinwart. Software, (2022)Related to: David Holzmüller, Viktor Zaverkin, Johannes Kästner, and Ingo Steinwart. A Framework and Benchmark for Deep Batch Active Learning for Regression, 2022. arXiv: 2203.09410.
D. Holzmüller, V. Zaverkin, J. Kästner, and I. Steinwart. Software, (2023)Related to: David Holzmüller, Viktor Zaverkin, Johannes Kästner, and Ingo Steinwart. A Framework and Benchmark for Deep Batch Active Learning for Regression, 2023. arXiv: 2203.09410.
V. Zaverkin, D. Holzmüller, I. Steinwart, and J. Kästner. Software, (2021)Related to: V. Zaverkin, D. Holzmüller, I. Steinwart, and J. Kästner, “Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments,” J. Chem. Theory Comput. 17, 6658–6670 (2021). doi: 10.1021/acs.jctc.1c00527.