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- Science Park 3 - Floor 3 - Room 327
- +43 732 2468 9398
- +43 732 2468 4539
- schimunek(at)ml.jku.at
Research Topics
- Deep Learning and Neural Networks
- Few-shot learning
- In-context learning
- Machine learning for drug discovery
- Hopfield Networks and Transformers
Selected Publications
- Context-enriched molecule representations improve few-shot drug discovery (2023). Schimunek, J., Seidl, P., Friedrich, L., Kuhn, D., Rippmann, F., Hochreiter, S., & Klambauer, G. In The Eleventh International Conference on Learning Representations
- A community effort to discover small molecule SARS-CoV-2 inhibitors (2023). Schimunek, J., Seidl, P., Elez, K., Hempel, T., Le, T., Noé, F., ... & Hermans, T.
- A generalized framework for embedding-based few-shot learning methods in drug discovery (2021). Schimunek, J., Friedrich, L., Kuhn, D., Rippmann, F., Hochreiter, S., & Klambauer, G. In ELLIS Machine Learning for Molecule Discovery Workshop.
- Comparative assessment of interpretability methods of deep activity models for hERG (2021). Schimunek, J., Friedrich, L., Kuhn, D., Hochreiter, S., Rippmann, F. and Klambauer, G. In 19th International Workshop on (Q)SAR in Environmental and Health Sciences.
- Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks (2020). Hofmarcher, M., Mayr, A., Rumetshofer, E., Ruch, P., Renz, P., Schimunek, J., ... & Klambauer, G.
