Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks M Hofmarcher, A Mayr, E Rumetshofer, P Ruch, P Renz, J Schimunek, ... arXiv preprint arXiv:2004.00979, 2020 | 63 | 2020 |
Context-enriched molecule representations improve few-shot drug discovery J Schimunek, P Seidl, L Friedrich, D Kuhn, F Rippmann, S Hochreiter, ... The Eleventh International Conference on Learning Representations, 2023 | 37 | 2023 |
A community effort in SARS‐CoV‐2 drug discovery J Schimunek, P Seidl, K Elez, T Hempel, T Le, F Noé, S Olsson, L Raich, ... Molecular Informatics 43 (1), e202300262, 2024 | 8 | 2024 |
A generalized framework for embedding-based few-shot learning methods in drug discovery J Schimunek, L Friedrich, D Kuhn, F Rippmann, S Hochreiter, ... ELLIS Machine Learning for Molecule Discovery Workshop, 2021 | 7 | 2021 |
Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks. 2020 M Hofmarcher, A Mayr, E Rumetshofer, P Ruch, P Renz, J Schimunek, ... DOI: https://doi. org/10.2139/ssrn 3561442, 2020 | 7* | 2020 |
Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences N Schmidinger, L Schneckenreiter, P Seidl, J Schimunek, PJ Hoedt, ... arXiv preprint arXiv:2411.04165, 2024 | 1 | 2024 |
Autoregressive activity prediction for low-data drug discovery J Schimunek, L Friedrich, D Kuhn, G Klambauer 5th Workshop on practical ML for limited/low resource settings, 2024 | | 2024 |
A community effort to discover small molecule SARS-CoV-2 inhibitors J Schimunek, P Seidl, K Elez, T Hempel, T Le, F Noé, S Olsson, L Raich, ... American Chemical Society (ACS), 2023 | | 2023 |