| In need of bias control: evaluating chemical data for machine learning in structure-based virtual screening J Sieg, F Flachsenberg, M Rarey Journal of chemical information and modeling 59 (3), 947-961, 2019 | 251 | 2019 |
| ProteinsPlus: a comprehensive collection of web-based molecular modeling tools K Schöning-Stierand, K Diedrich, C Ehrt, F Flachsenberg, J Graef, J Sieg, ... Nucleic Acids Research 50 (W1), W611-W615, 2022 | 59 | 2022 |
| StructureProfiler: an all-in-one tool for 3D protein structure profiling A Meyder, S Kampen, J Sieg, R Fährrolfes, NO Friedrich, F Flachsenberg, ... Bioinformatics 35 (5), 874-876, 2019 | 6 | 2019 |
| Searching similar local 3D micro-environments in protein structure databases with MicroMiner J Sieg, M Rarey Briefings in Bioinformatics 24 (6), bbad357, 2023 | 2 | 2023 |
| Modeling with alternate locations in X-ray protein structures T Gutermuth, J Sieg, T Stohn, M Rarey Journal of Chemical Information and Modeling 63 (8), 2573-2585, 2023 | 2 | 2023 |
| Analyzing structural features of proteins from deep‐sea organisms J Sieg, CC Sandmeier, J Lieske, A Meents, C Lemmen, WR Streit, ... Proteins: Structure, Function, and Bioinformatics 90 (8), 1521-1537, 2022 | 2 | 2022 |
| MolPipeline: A python package for processing molecules with RDKit in scikit-learn J Sieg, CW Feldmann, J Hemmerich, C Stork, F Sandfort, P Eiden, ... | | 2024 |
| Transformers for molecular property prediction: Lessons learned from the past five years A Sultan, J Sieg, M Mathea, A Volkamer arXiv preprint arXiv:2404.03969, 2024 | | 2024 |
| Machine learning in the context of bioactivity J Sieg, M Rarey ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY 257, 2019 | | 2019 |
