Here, you will find a list of all articles and pre-prints authored by members of the AiChemist Consortium. You can also check out our Google Scholar, which is more regularly updated.
(1) Tetko, I. Tox24 Challenge. Chem. Res. Tox 2024. https://doi.org/10.1021/acs.chemrestox.4c00192
(2) 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.
(3) 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. https://doi.org/10.1186/s13321-025-00982-w.
(4) 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.
(5) Hunklinger, A.; Ferruz, N. Toward the Explainability of Protein Language Models for Sequence Design. arXiv 2025. https://doi.org/10.48550/arXiv.2506.19532
(6) Cirino, T.; Pinto, L.; Iwan, M. et al. Consensus Modeling Strategies for Predicting Transthyretin Binding Affinity from Tox24 Challenge Data. Chem. Res. Toxicol. 2025. https://doi.org/10.1021/acs.chemrestox.5c00018.
(7) Eytcheson, S. A.; Tetko, I. V. Which Modern AI Methods Provide Accurate Predictions of Toxicological Endpoints? Analysis of Tox24 Challenge Results. Chem. Res. Tox. 2025. https://doi.org/10.1021/acs.chemrestox.5c00273.
(8) Tetko, I.V.; Clevert, D.A. Advanced Machine Learning for Innovative Drug Discovery. J. Cheminform. 2025. https://doi.org/10.1186/s13321-025-01061-w
(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. https://doi.org/10.1038/s44318-025-00520-2.
(12) Krüger, F. P.; Österbacka, M; Kabeshov, M.; Engkvist, O.; Tetko, I. MolEncoder: Improved Masked Language Modeling for Molecules. International Conference on Artificial Neural Networks 2025. https://link.springer.com/chapter/10.1007/978-3-032-04552-2_6
(13) Krüger, F. P.; Österbacka, M; Kabeshov, M.; Engkvist, O.; Tetko, I. MolEncoder: Improved Masked Language Modeling for Molecules. Digital Discovery 2025. https://link.springer.com/chapter/10.1007/978-3-032-04552-2_6
(14) Park, J.; Rashid, S.; Copsey, H.; Tran, L. ;Zabeo, A.; Hristozov, D.; Gakis, G. P.; Charitidis, C.; Yoon, S.; Shin H.K. NanoToxRadar: A Multitarget Nano-QSAR Model for Predicting the Cytotoxicity Values of Multicomponent Nanoparticles. ACS Nanoscience Au. 2025. https://doi.org/10.1021/acsnanoscienceau.5c00035
(15) Viganò, E. L.; Iwan, M.; Colombo, E.; Ballabio, D.; Roncaglioni, A. Mixture of experts for multitask learning in cardiotoxicity assessment. J. Cheminformatics 2025. https://doi.org/10.1186/s13321-025-01072-7.
(16) Alcaide, E.; Gao, Z.; Ke, G.; Li, Y.; Zhang, L.; Zheng, H.; Zhou. G. Uni-Mol Docking V2: Towards Realistic and Accurate Binding Pose Prediction. International Conference on Artificial Neural Networks 2025. https://link.springer.com/chapter/10.1007/978-3-032-04552-2_5