Publications

Here, you will find a list of all articles and pre-prints authored by members of the AiChemist Consortium.

(1)   Hartog, P. B. R.; Krüger, F.; Genheden, S.; Tetko, I. V. Using Test-Time Augmentation to Investigate Explainable AI: Inconsistencies between Method, Model and Human Intuition. J. Cheminformatics 2024, 16(1), 39. https://doi.org/10.1186/s13321-024-00824-1.

(2)   Hunklinger, A.; Hartog, P.; Šícho, M.; Godin, G.; Tetko, I. V. The openOCHEM Consensus Model Is the Best-Performing Open-Source Predictive Model in the First EUOS/SLAS Joint Compound Solubility Challenge. SLAS Discov. 2024, 29 (2). https://doi.org/10.1016/j.slasd.2024.01.005.

(3)   Stocco, F.; Artigues-Lleixà, M.; Hunklinger, A.; Widatalla, T.; Güell, M.; Ferruz, N. Guiding Generative Protein Language Models with Reinforcement Learning. arXiv 2025. https://doi.org/10.48550/arXiv.2412.12979.

(4)   Krüger, F. P.; Östman, J.; Mervin, L.; Tetko, I. V.; Engkvist, O. Publishing Neural Networks in Drug Discovery Might Compromise Training Data Privacy. J. Cheminformatics 2025, 17 (1), 38. https://doi.org/10.1186/s13321-025-00982-w.

(5)   Goldstein, D.; Alcaide, E.; Lu, J.; Cheah, E. RADLADS: Rapid Attention Distillation to Linear Attention Decoders at Scale. arXiv 2025. https://doi.org/10.48550/arXiv.2505.03005.

(6)   Hunklinger, A.; Ferruz, N. Toward the Explainability of Protein Language Models for Sequence Design. arXiv 2025. https://doi.org/10.48550/arXiv.2506.19532

(7)   Cirino, T.; Pinto, L.; Iwan, M. et al. Consensus Modeling Strategies for Predicting Transthyretin Binding Affinity from Tox24 Challenge Data. Chem. Res. Toxicol. 2025, 38 (6), 1061–1071. https://doi.org/10.1021/acs.chemrestox.5c00018.

(8)   Eytcheson, S. A.; Tetko, I. V. Which Modern AI Methods Provide Accurate Predictions of Toxicological Endpoints? Analysis of Tox24 Challenge Results. ChemRxiv 2025. https://doi.org/10.26434/chemrxiv-2025-7k7x3.

(9)   Ball, M.; Horvath, D.; Kogej, T.; Kabeshov, M.; Varnek, A. Predicting Reaction Conditions: A Data-Driven  Perspective. Chem. Sci. 2025. https://doi.org/10.1039/D5SC03045E.

(10)  Krüger, F. P.; Österbacka, N.; Kabeshov, M.; Engkvist, O.; Tetko, I. MolEncoder: Towards Optimal Masked Language Modeling for Molecules. ChemRxiv. 2025. https://doi.org/10.26434/chemrxiv-2025-h4w9d.

(11)   Singh, I.; Onogi, Y.; Menezes, F.; Khasanova, D.; Kang, L.; Wang, C.; Ruiz-Trave, J.; Sharma, S.; Khalil, A.; Reichenbach, V. K.; Shi, Y.; Flatley, A.; Yan, X.; Israel, A.; Dragano, N. R. V.; Aguilar-Pimentel, J. A.; Hoffmann, A.; Ghosh, A.; Noé, F.; Wolfrum, C.; Cucuruz, S.; König, A.-C.; Burtscher, I.; Hauck, S. M.; Lickert, H.; Hofmann, S. M.; Feederle, R.; Schriever, S. C.; Hernandez-Bautista, R.; Sancar, G.; Cebrian-Serrano, A.; Tetko, I.; Fuchs, H.; Gailus-Durner, V.; Blüher, M.; Hrabě de Angelis, M.; Ussar, S. NRAC Controls CD36-Mediated Fatty Acid Uptake in Adipocytes and Lipid Clearance in Vivo. EMBO J. 2025, 1–29. https://doi.org/10.1038/s44318-025-00520-2.