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. Task-conditioned modeling of drug-target interactions. In ELLIS Machine Learning for Molecules Discovery Workshop. November 28, 2022.
(12)    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. 
(13)    Andronov, M.; Voinarovska, V.; Andronova, N.; Wand, M.; Clevert, D.-A.; Schmidhuber, J.  Reagent Prediction with a Molecular Transformer Improves Reaction Data Quality. Chem. Sci. 2023.
(14) 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, 
(15) Cremer, J.; Medrano Sandonas, L.; Tkatchenko, A.; Clevert, D.A.: De Fabritiis, G. Equivariant Graph Neural Networks for Toxicity Prediction. Chem. Res. Toxicol. 2023. 
(16) Sarkis, M.; Fallani, A.;  Tkatchenko, A. Modeling Non-Covalent Interatomic Interactions on a Photonic Quantum Computer. Physical Review Research. 2023. 10.1103/PhysRevResearch.5.043072
(17)  Sanchez-Fernandez, A.; Rumetshofer, E.;  Hochreiter, S.; Klambauer, G. CLOOME:contrastive learning unlocks bioimaging databases for queries with chemical structures. Nature Communications. 2023. 
(18) Le, T.; Cremer, J.; Noé, F.; Clevert, D-A.; Schütt, K. Navigating the design space of equivariant diffusion-based generative models for de novo 3D molecule generation. arXiv. 2023.
(19)  Voinarovska, V.; Kabeshov, M.; Dudenko, D.; Genheden, S.; Tetko, I.V. When Yield Prediction Does Not Yield Prediction: An Overview of the Current Challenges. J. Chem. Inf. Model. 2024.
(20)  Andronov, M.; Andronova, N., Wand, M., Schmidhuber, J., Clevert, D-A. A reagent-driven visual method for analyzing chemical reaction data. arXiv. 2024.  
(21) Kopp, A.; Hartog, P.; Šícho, M.; Godin, G.; Tetko, I. The openOCHEM consensus model is the best-performing open-source predictive model in the First EUOS/SLAS Joint Compound Solubility Challenge. SLAS Discovery. 2024.
(22) 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. Digital Discovery. 2024. 
(23) Hartog, P.; Krüger, F.; Genheden, S.; Tetko, I.V. Using test-time augmentation to investigate explainable AI: inconsistencies between method, model and human intuition.ChemRxiv. 2024. 
(24) Bernatavicius, A.; Šícho, M.; Janssen, A.; Hassen, A.K.; Preuss, M.; van Westen, G. AlphaFold meets de novo drug design: leveraging structural protein information in multi-target molecular generative models. ChemRxiv. 2024. 
(25) Ha, S.; Leuschner, L.; Czodrowski, P. FSL-CP: a benchmark for small molecule activity few-shot prediction using cell microscopy images. Digital Discovery. 2024. DOI: 10.1039/d3dd00205e
(26) Svensson, E., Hoedt, P.-J., Hochreiter, S., Klambauer, G. HyperPCM: Robust Task-Conditioned Modeling of Drug-Target Interactions. J. Chem. Inf. Model. 2024.
(27) Hassen, A.K.; Šícho, M.; van Aalst, Y.J.; Huizenga, M.C.W.; Reynolds, D.N.R.; Luukkonen, S.; Bernatavicius, A.; Clevert, D-A.; Janssen, A.P.A.; van Westen, G.J.P.; Preuss, M. Generate What You Can Make: Achieving in-house synthesizability with readily available resources in de novo drug design. ChemRvix. 2024.