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Institute for Machine Learning
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Johannes Schimunek, LAss

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.
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Johannes Kepler University Linz

Altenberger Straße 69

4040 Linz, Austria

Telephone +43 732 2468 0
Fax +43 732 2468 4929
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