See also  project publications at Google Scholar.

(1)    Simm, J.; Arany, A.; De Brouwer, E.; Moreau, Y. Expressive Graph Informer Networks. In Machine Learning, Optimization, and Data Science; Nicosia, G., Ojha, V., La Malfa, E., La Malfa, G., Jansen, G., Pardalos, P. M., Giuffrida, G., Umeton, R., Eds.; Springer International Publishing: Cham, 2022; pp 198–212.
(2)    Sanchez-Fernandez, A.; Rumetshofer, E.; Hochreiter, S.; Klambauer, G. Contrastive Learning of Image- and Structure-Based Representations in Drug Discovery; 2022.
(3)    Roland, T.; Böck, C.; Tschoellitsch, T.; Maletzky, A.; Hochreiter, S.; Meier, J.; Klambauer, G. Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests. J. Med. Syst. 2022, 46 (5), 23.
(4)    Klotz, D.; Kratzert, F.; Gauch, M.; Keefe Sampson, A.; Brandstetter, J.; Klambauer, G.; Hochreiter, S.; Nearing, G. Uncertainty Estimation with Deep Learning  for Rainfall–Runoff Modeling. Hydrol Earth Syst Sci 2022, 26 (6), 1673–1693.
(5)    Vall, A.; Sabnis, Y.; Shi, J.; Class, R.; Hochreiter, S.; Klambauer, G. The Promise of AI for DILI Prediction. Front. Artif. Intell. 2021, 4.
(6)    Kratzert, F.; Klotz, D.; Hochreiter, S.; Nearing, G. S. A Note on Leveraging Synergy in Multiple Meteorological Data Sets with Deep Learning for Rainfall–Runoff Modeling. Hydrol Earth Syst Sci 2021, 25 (5), 2685–2703.
(7)    Kim, P. T.; Winter, R.; Clevert, D.-A. Unsupervised Representation Learning for Proteochemometric Modeling. Int. J. Mol. Sci. 2021, 22 (23).
(8)    Hoedt, P.-J.; Kratzert, F.; Klotz, D.; Halmich, C.; Holzleitner, M.; Nearing, G.; Hochreiter, S.; Klambauer, G. MC-LSTM: Mass-Conserving LSTM. ArXiv E-Prints 2021, arXiv:2101.05186.
(9)    Gauch, M.; Kratzert, F.; Klotz, D.; Nearing, G.; Lin, J.; Hochreiter, S. Rainfall–Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network. Hydrol Earth Syst Sci 2021, 25 (4), 2045–2062.
(10)    Clevert, D.-A.; Le, T.; Winter, R.; Montanari, F. Img2Mol – Accurate SMILES Recognition from Molecular Graphical Depictions. Chem. Sci. 2021, 12 (42), 14174–14181.
(11)    Svensson, E., Hoedt, P.-J., Hochreiter, S., Klambauer, G. Robust task-specific adaption of models for drug-target interaction prediction. In NeurIPS2023 AI4Science Workshop. November 28, 2022.
(12)   Svensson, E., Hoedt, P.-J., Hochreiter, S., Klambauer, G. Task-conditioned modeling of drug-target interactions. In ELLIS Machine Learning for Molecules Discovery Workshop. November 28, 2022.
(13)    Hassen, A. K.; Torren-Peraire, P.; Genheden, S.; Verhoeven, J.; Preuss, M.; Tetko, I. V. Mind the Retrosynthesis Gap: Bridging the Divide between Single-Step and Multi-Step Retrosynthesis Prediction; 2022.
(14)    Andronov, M.; Voinarovska, V.; Andronova, N.; Wand, M.; Clevert, D.-A.; Schmidhuber, J.  Reagent Prediction with a Molecular Transformer Improves Reaction Data Quality. 2023.  Chem. Sci.
(15) Radaeva, M.; Ban, F.; Zhang, F.; LeBlanc, E.; Lallous, N.; Rennie, P.S.; Gleave, M.E.; Cherkasov, A. Development of Novel Inhibitors Targeting the D-Box of the DNA Binding Domain of Androgen Receptor. Int. J. Mol. Sci. 2021, 22, 2493,
(16) Cremer, J.; Medrano Sandonas, L.; Tkatchenko, A.; Clevert, D.A.: De Fabritiis, G. Equivariant Graph Neural Networks for Toxicity Prediction, , 2023.
(17) Torren-Peraire, P.; Hassen, A. K.;  Genheden, S.; Verhoeven, J.; Clevert, D.-A.; Preuss, M.; Tetko, I. V. Models Matter: The Impact of Single-Step Retrosynthesis on Synthesis Planning; 2023.
(18) Kopp, A.; Hartog, P.; Šícho, M.; Godin, G.; Tetko, I. openOCHEM consensus model wins Kaggle First EUOS/SLAS Joint Compound Solubility Challenge; 2023,
(19) Sarkis, M.; Fallani, A.;  Tkatchenko, A. Modeling Non-Covalent Interatomic Interactions on a Photonic Quantum Computer,  2023,
(20)  Sanchez-Fernandez, A.; Rumetshofer, E.;  Hochreiter, S.; Klambauer, G. CLOOME:contrastive learning unlocks bioimaging databases for queries with chemical structures; 2023.