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. https://www.springerprofessional.de/expressive-graph-informer-networks/20089896
(2) Sanchez-Fernandez, A.; Rumetshofer, E.; Hochreiter, S.; Klambauer, G. Contrastive Learning of Image- and Structure-Based Representations in Drug Discovery; 2022. https://openreview.net/forum?id=OdXKRtg1OG
(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. https://link.springer.com/article/10.1007/s10916-022-01807-1.
(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. https://doi.org/10.5194/hess-26-1673-2022.
(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. https://doi.org/10.3389/frai.2021.638410
(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. https://doi.org/10.5194/hess-25-2685-2021.
(7) Kim, P. T.; Winter, R.; Clevert, D.-A. Unsupervised Representation Learning for Proteochemometric Modeling. Int. J. Mol. Sci. 2021, 22 (23). https://doi.org/10.3390/ijms222312882.
(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. https://doi.org/10.5194/hess-25-2045-2021.
(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. https://doi.org/10.1039/D1SC01839F.
(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. https://moleculediscovery.github.io/workshop2022/
(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. https://openreview.net/forum?id=LjdtY0hM7tf
(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. https://doi.org/10.1039/D2SC06798F
(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, https://doi.org/10.3390/ijms22052493
(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. https://doi.org/10.1021/acs.chemrestox.3c00032
(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) Martinelli, J.; Nahal, Y.; Lê, D.; Engkvist, O.; Kaski, S. Leveraging expert feedback to align proxy and ground truth rewards in goal-oriented molecular generation. In NeurIPS2023 Workshop AI4D3. 2023. https://openreview.net/forum?id=KWIM7ZNYxb. Link to full workshop paper.
(18) Sanchez-Fernandez, A.; Rumetshofer, E.; Hochreiter, S.; Klambauer, G. CLOOME:contrastive learning unlocks bioimaging databases for queries with chemical structures. Nature Communications. 2023. https://doi.org/10.1038/s41467-023-42328-w
(19) 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. https://doi.org/10.48550/arXiv.2309.17296
(20) 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. https://doi.org/10.1021/acs.jcim.3c01524
(21) Andronov, M.; Andronova, N., Wand, M., Schmidhuber, J., Clevert, D-A. A reagent-driven visual method for analyzing chemical reaction data. arXiv. 2024. https://doi.org/10.26434/chemrxiv-2024-q9tc4
(22) 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. https://doi.org/10.1016/j.slasd.2024.01.005
(23) 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. https://doi.org/10.1039/D3DD00252G
(24) Hartog, P.; Krüger, F.; Genheden, S.; Tetko, I.V. Using test-time augmentation to investigate explainable AI: inconsistencies between method, model and human intuition. Journal of Cheminformatics. 2024. https://doi.org/10.1186/s13321-024-00824-1
(25) 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. https://doi.org/10.26434/chemrxiv-2024-60tc7
(26) 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
(27) Svensson, E., Hoedt, P.-J., Hochreiter, S., Klambauer, G. HyperPCM: Robust Task-Conditioned Modeling of Drug-Target Interactions. J. Chem. Inf. Model. 2024. https://doi.org/10.1021/acs.jcim.3c01417
(28) 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. https://doi.org/10.26434/chemrxiv-2024-wtjt6
(29) Borowa, A., Rymarczyk, D., Zyła, M., Kanduła, M., Sánchez-Fernández, A., Rataj, K., Struski, Ł., Tabor, J., and Zielinski, B. Decoding phenotypic screening: A comparative analysis of image representations. Computational and Structural Biotechnology Journal, 2024. https://doi.org/10.1016/j.csbj.2024.02.022
(30) Tan, L; Hirte, S.; Palmacci, V.; Stork, C.; Kirchmair, J. Tackling assay interference associated with small molecules. Nature Reviews Chemistry. 2024. https://doi.org/10.1038/s41570-024-00593-3
(31) Lakshidaa Saigiridharan, Alan Kai Hassen, Helen Lai, Paula Torren-Peraire, Ola Engkvist, and Samuel Genheden. Aizynthfinder 4.0: developments based on learnings from 3 years of industrial application. Journal of Cheminformatics. 2024. https://doi.org/10.1186/s13321-024-00860-x
(32) Cremer, J., Le, T., Noé, F., Clevert, D-A., Schütt, K. T. PILOT: Equivariant diffusion for pocket conditioned de novo ligand generation with multi-objective guidance via importance sampling. Chemical Science, 2024. https://doi.org/10.1039/D4SC03523B
(33) Palmacci, V., Hirte, S., Hernandez Gonzales, J.E., Montanari, F., Kirchmair, J. Statistical approaches enabling technology-specific assay interference prediction from large screening data sets. Artificial Intelligence in the Life Sciences. 2024. https://doi.org/10.1016/j.ailsci.2024.100099
(34) Ha, S., Jaensch, S., Freitas, L.G.A., Herman, D., Czodrowski, P.. and Ceulemans, H. Low concentration cell painting images enable the identification of highly potent compounds. Scientific Reports. 2024. https://doi.org/10.1038/s41598-024-75401-5
(35) Rydholm, E., Bastys, T., Svensson, E., Kannas, C., Engkvist, O., and Kogej, T. Expanding the chemical space using a chemical reaction knowledge graph. Digital Discovery. 2024. https://doi.org/10.1039/D3DD00230F
(36) Sandonas, L. M., Rompaey, D., Fallani, A., Hilfiker, M., Hahn, D., Perez-Benito, L., Verhoeven, J., Tresadern, G., Wegner, J. K., Ceulemans, H., and Tkatchenko A. Dataset for quantum-mechanical exploration of conformers and solvent effects in large drug-like molecules. Scientific Data. 2024. https://doi.org/10.1038/s41597-024-03521-8
(37) Andronov, M., Andronova, N., Wand, M., Schmidhuber, J., Clevert, D-A. Accelerating the inference of string generation-based chemical reaction models for industrial applications. arXiv. 2024. https://doi.org/10.48550/arXiv.2407.09685
(38) Friesacher, H.R., Engkvist, O., Mervin, L., Moreau, Y. and Arany, A. Achieving well-informed decision-making in drug discovery: A comprehensive calibration study using neural network-based structure-activity models. arXiv. 2024. https://doi.org/10.48550/arXiv.2407.14185
(39) Fallani, A., Sandonas, L. M., and Tkatchenko, A. Inverse mapping of quantum properties to structures for chemical space of small organic molecules. Nature Communications. 2024. https://doi.org/10.1038/s41467-024-50401-1
(40) Nahal, Y., Menke, J., Martinelli, J., Heinonen, M., Kabeshov, M., Janet, J. P., Nittinger, E., Engkvist, O. and Kaski, S. Human-in-the-loop active learning for goal-oriented molecule generation. ChemRxiv. 2024. https://doi.org/10.26434/chemrxiv-2024-623lx
(41) Menke, J., Nahal, Y., Bjerrum, E. J., Kabeshov, M., Kaski, S., and Engkvist, O. Metis-a python-based user interface to collect expert feedback for generative chemistry models. Journal of Cheminformatics. 2024. https://doi.org/10.1186/s13321-024-00892-3
(42) Hartog, P., Westerlund, A., Tetko, I.V., Genheden, S. Investigations into the efficiency of computer-aided synthesis planning. ChemRxiv. 2024. https://doi.org/10.26434/chemrxiv-2024-q2v87
(43) Lai, H., Kannas C., Kai Hassen, A., Rydholm, E., Westerlund, A., Clevert, D-A., et al. Multi-objective synthesis planning by means of Monte Carlo tree search. ChemRxiv. 2024. https://doi.org/10.26434/chemrxiv-2024-3tzs5-v2
(43) Friesacher, H.R., Svensson, E., Arany, A., Mervin, L., Engkvist, O. Temporal Evaluation of Probability Calibration with Experimental Errors. In: Clevert, DA., Wand, M., Malinovská, K., Schmidhuber, J., Tetko, I.V. (eds) AI in Drug Discovery. ICANN 2024. Lecture Notes in Computer Science, vol 14894. Springer, Cham. 2024. https://doi.org/10.1007/978-3-031-72381-0_2
(44) Andronov, M., Andronova, N., Wand, M., Schmidhuber, J., Clevert, DA. Curating Reagents in Chemical Reaction Data with an Interactive Reagent Space Map. In: Clevert, DA., Wand, M., Malinovská, K., Schmidhuber, J., Tetko, I.V. (eds) AI in Drug Discovery. ICANN 2024. Lecture Notes in Computer Science, vol 14894. Springer, Cham. 2024. https://doi.org/10.1007/978-3-031-72381-0_3
(45) Cremer, J., Le, T., Clevert, DA., Schütt, K.T. Latent-Conditioned Equivariant Diffusion for Structure-Based De Novo Ligand Generation. In: Clevert, DA., Wand, M., Malinovská, K., Schmidhuber, J., Tetko, I.V. (eds) AI in Drug Discovery. ICANN 2024. Lecture Notes in Computer Science, vol 14894. Springer, Cham. 2024. https://doi.org/10.1007/978-3-031-72381-0_4
(46) Hilfiker, M., Sandonas, L.M., Klähn, M., Engkvist, O., Tkatchenko, A. Leveraging Quantum Mechanical Properties to Predict Solvent Effects on Large Drug-Like Molecules. In: Clevert, DA., Wand, M., Malinovská, K., Schmidhuber, J., Tetko, I.V. (eds) AI in Drug Discovery. ICANN 2024. Lecture Notes in Computer Science, vol 14894. Springer, Cham. 2024. https://doi.org/10.1007/978-3-031-72381-0_5
(47) Nahal, Y. et al. Towards Interpretable Models of Chemist Preferences for Human-in-the-Loop Assisted Drug Discovery. In: Clevert, DA., Wand, M., Malinovská, K., Schmidhuber, J., Tetko, I.V. (eds) AI in Drug Discovery. ICANN 2024. Lecture Notes in Computer Science, vol 14894. Springer, Cham. 2024. https://doi.org/10.1007/978-3-031-72381-0_6
(48) Fallani, A., Arjona-Medina, J., Chernichenko, K., Nugmanov, R., Wegner, J.K., Tkatchenko, A. Atom-Level Quantum Pretraining Enhances the Spectral Perception of Molecular Graphs in Graphormer. In: Clevert, DA., Wand, M., Malinovská, K., Schmidhuber, J., Tetko, I.V. (eds) AI in Drug Discovery. ICANN 2024. Lecture Notes in Computer Science, vol 14894. Springer, Cham. 2024. https://doi.org/10.1007/978-3-031-72381-0_7
(49) Masood, M.A., Kaski, S., Ceulemans, H., Herman, D., Heinonen, M. Balancing Imbalanced Toxicity Models: Using MolBERT with Focal Loss. In: Clevert, DA., Wand, M., Malinovská, K., Schmidhuber, J., Tetko, I.V. (eds) AI in Drug Discovery. ICANN 2024. Lecture Notes in Computer Science, vol 14894. Springer, Cham. 2024. https://doi.org/10.1007/978-3-031-72381-0_8
(50) Hartog, P., Svensson, E., Mervin, L., Genheden, S., Engkvist, O., Tetko, I.V. Registries in Machine Learning-Based Drug Discovery: A Shortcut to Code Reuse. In: Clevert, DA., Wand, M., Malinovská, K., Schmidhuber, J., Tetko, I.V. (eds) AI in Drug Discovery. ICANN 2024. Lecture Notes in Computer Science, vol 14894. Springer, Cham. 2024. https://doi.org/10.1007/978-3-031-72381-0_9
(51) Svensson, E., Friesacher, H.R., Arany, A., Mervin, L., Engkvist, O. Temporal Evaluation of Uncertainty Quantification Under Distribution Shift. In: Clevert, DA., Wand, M., Malinovská, K., Schmidhuber, J., Tetko, I.V. (eds) AI in Drug Discovery. ICANN 2024. Lecture Notes in Computer Science, vol 14894. Springer, Cham. 2025. https://doi.org/10.1007/978-3-031-72381-0_11
(52) Masood, M.A., Cui, T., Kaski, S. Deep Bayesian Experimental Design for Drug Discovery. In: Clevert, DA., Wand, M., Malinovská, K., Schmidhuber, J., Tetko, I.V. (eds) AI in Drug Discovery. ICANN 2024. Lecture Notes in Computer Science, vol 14894. Springer, Cham. 2024. https://doi.org/10.1007/978-3-031-72381-0_12
(53) Fallani, A., Nugmanov, R., Arjona-Medina, J., Wegner, J.K, Tkatchenko, A., Chernichenko, K. Pretraining Graph Transformers with Atom-in-a-Molecule Quantum Properties for Improved ADMET Modeling. arXiv. 2024. https://doi.org/10.48550/arXiv.2410.08024