Publications
Peer-reviewed publications and Preprints
2026
Zubia-Aranburu J, Gardin A, Paffen L, Tollemeto M, Alberdi A, et al. (2026). Predicting DNA origami stability in physiological media by machine learning. Small Structures 7, e202500784.
Hiltscher M, Bianciotto M, Grisoni F (2026). Explaining What Matters: Faithfulness in Molecular Deep Learning. ChemRxiv.
Iwan M, Roncaglioni A, Grisoni F (2026). Beyond Molecular Structure: Investigating Demographic Factors in Drug-Induced Cardiotoxicity Prediction Models. ChemRxiv.
van Tilborg D, Grisoni F (2026). ChemLint: Conversational Cheminformatics with Large Language Models. ChemRxiv.
Criscuolo E, Grisoni F (2026). Towards a physically interpretable symbolic language of molecular recognition. ChemRxiv.
2025
Özçelik R, Grisoni F (2025). How evaluation choices distort the outcome of generative drug discovery. Journal of Cheminformatics 17, 169.
Leurs YHA, Van Den Hout W, Gardin A, et al. (2025). Automated navigation of condensate phase behavior with active machine learning. Nature Communications 16, 9598.
Nißl B, Mülbaier M, Grisoni F, Trauner D, et al. (2025). The chemistry and biology of the tetrodotoxin natural product family. Angewandte Chemie International Edition 64, e202502404.
Birolo R, Özçelik R, Aramini A, Gobetto R, Chierotti MR, Grisoni F (2025). Deep Supramolecular Language Processing for Co-Crystal Prediction. Angewandte Chemie International Edition 64, e202507835.
Özçelik R, Brinkmann H, Criscuolo E, Grisoni F (2025). Generative Deep Learning for de Novo Drug Design — A Chemical Space Odyssey. Journal of Chemical Information and Modeling 65, 7352.
Rossen L, Sirockin F, Schneider N, Grisoni F (2025). Scaffold hopping with generative reinforcement learning. Journal of Chemical Information and Modeling 65, 6513.
Mastrolorito F, Ciriaco F, Togo MV, Gambacorta N, Trisciuzzi D, Altomare CD, Grisoni F, Nicolotti O (2025). fragSMILES as a chemical string notation for advanced fragment and chirality representation. Communications Chemistry 8, 26.
Brinkmann H, Argante A, Ter Steege H, Grisoni F (2025). Going beyond SMILES enumeration for data augmentation in generative drug discovery. Digital Discovery 4, 2752.
Mastrolorito F, Ciriaco F, Nicolotti O, Grisoni F (2025). Enhancing deep chemical reaction prediction with advanced chirality and fragment representation. Chemical Communications 61, 18344.
van Weesep L, Özçelik R, Pennings M, Criscuolo E, Ottmann C, et al. (2025). Identifying 14-3-3 interactome binding sites with deep learning. Digital Discovery 4, 2602.
Özçelik R, Grisoni F (2025). A hitchhiker’s guide to deep chemical language processing for bioactivity prediction. Digital Discovery 4, 316.
Özçelik R, van Weesep L, de Ruiter S, Grisoni F (2025). peptidy: a light-weight Python library for peptide representation in machine learning. Bioinformatics Advances 5, vbaf058.
Nana Teukam YG, Zipoli F, Laino T, Criscuolo E, Grisoni F, Manica M (2025). Integrating genetic algorithms and language models for enhanced enzyme design. Briefings in Bioinformatics 26, bbae675.
Özçelik R, Grisoni F (2025). De Novo Drug Design by Chemical Language Modeling. An Introduction to Generative Drug Discovery, 45.
van Tilborg D, Rossen L, Grisoni F (2025). Molecular deep learning at the edge of chemical space. ChemRxiv.
Özçelik R, de Ruiter S, Grisoni F (2025). Look the Other Way: Designing ‘Positive’ Molecules with Negative Data via Task Arithmetic. arXiv.
2024
Bessadok A, Grisoni F (2024). An explainable foundation model for drug repurposing. Nature Medicine 30, 3422.
van Tilborg D, Grisoni F (2024). Traversing chemical space with active deep learning for low-data drug discovery. Nature Computational Science 4, 786.
Izquierdo-Lozano C, van Noort N, van Veen S, Tholen MM, Grisoni F, Albertazzi L (2024). nanoFeatures: a cross-platform application to characterize nanoparticles from super-resolution microscopy images. Nanoscale 16, 20885.
Özçelik R, De Ruiter S, Criscuolo E, Grisoni F (2024). Chemical Language Modeling with Structured State Space Models. Nature Communications 15, 6176.
van Tilborg, D, Brinkmann H, Criscuolo E, Rossen L, Özçelik R, Grisoni F (2024). Deep learning for low-data drug discovery: hurdles and opportunities. Current Opinion in Structural Biology, 86, 102818.
Teukam, YGN, Zipoli F, Laino T, Criscuolo E, Grisoni F, Manica M (2024). Integrating Genetic Algorithms and Language Models for Enhanced Enzyme Design. Briefings in Bioinformatics (just accepted).
Ortiz-Perez A+, van Tilborg D+, van der Meel, R, Grisoni F, Albertazzi L (2024). Machine learning-guided high throughput nanoparticle design. Digital Discovery 3, 1280.
van der Meel R, Grisoni F, Mulder WJ (2024). Lipid discovery for mRNA delivery guided by machine learning. Nature Materials 23, 880.
Faquetti ML, Slappendel L, Bigonne H, Aichinger G, Grisoni F, Schneider P, Schneider G, Burden A, Sturla S (2024). Baricitinib and tofacitinib off-target profile, with a focus on Alzheimer’s disease. Alzheimer’s & Dementia: Translational Research & Clinical Interventions, 10.
Criscuolo E, Özçelik R, van Tilborg D, Grisoni F (2024). The surprising ineffectiveness of molecular dynamics coordinates for predicting bioactivity with machine learning. ChemRxiv.
2023
Boldini D, Ballabio D, Consonni V, Todeschini R, Grisoni F, Sieber S (2023). Effectiveness of molecular fingerprints for exploring the chemical space of natural products. Journal of Cheminformatics 16, 35.
Moret M, Pachon Angona I, Cotos L, Yan S, Atz K, Brunner C, Baumgartner M, Grisoni F, Schneider G (2023). Leveraging molecular structure and bioactivity with chemical language models for de novo drug design. Nature Communications 14, 114.
Mullowney MW, Duncan KR, Elsayed SS, et al. (2023). Artificial intelligence for natural product drug discovery. Nature Reviews Drug Discovery 1, 22.
Boldini D, Grisoni F, Kuhn D, Friedrich L and Sieber AS (2023). Practical guidelines for the use of gradient boosting for molecular property prediction. J. Cheminform. 15, 73.
Grisoni F (2023). Chemical language models for de novo drug design: Challenges and opportunities. Current Opinion in Structural Biology 79, 102527.
Ballarotto M, Willems S, Stiller T, Nawa F, Marschner JA, Grisoni F and Merk D (2023). De Novo Design of Nurr1 Agonists via Fragment-Augmented Generative Deep Learning in Low-Data Regime. Journal of Medicinal Chemistry 66, 12.
Özçelik R, van Tilborg D, Jiménez-Luna J and Grisoni F (2023). Structure‐based Drug discovery with Deep Learning. ChemBioChem e202200776.
Deckers J, Anbergen T, Hokke AM, de Dreu A, Schrijver DP, de Bruin K, Toner YC, Beldman, TJ, Spangler JB, de Greef TF, Grisoni F, van der Meel R, Joosten LAB, Merkx M, Netea MG and Mulder WJM (2023). Engineering cytokine therapeutics. Nature Reviews Bioengineering 1, 286.
Volkamer A, Riniker S, Nittinger E, Lanini J, Grisoni F, Evertsson E, Schneider N (2023). Machine Learning for Small Molecule Drug Discovery in Academia and Industry. Artificial Intelligence in the Life Sciences, 100056.
2022
van Tilborg D, Alenicheva A and Grisoni F (2022). Exposing the limitations of molecular machine learning with activity cliffs. Journal of Chemical Information and Modeling 62, 5938.
Ortiz-Perez A, Izquierdo-Lozano C, Meijers R, Grisoni F and Albertazzi L (2023). Identification of fluorescently-barcoded nanoparticles using machine learning. Nanoscale Advances 5, 2307.
2021
Grisoni F, Huisman BJ, Button AL, Moret M, Atz K, Merk D, Schneider G. (2021). Combining generative artificial intelligence and on-chip synthesis for de novo drug design. Science Advances 7 , e3338.
Moret M, Helmstädter M, Grisoni F, Schneider G, Merk D (2021). Beam search for automated design and scoring of novel ROR ligands with machine intelligence. Angewandte Chemie International Edition 60, 19477.
Faquetti, M. L., Grisoni, F., Schneider, P., Schneider, G., & Burden, A. M. (2022). Identification of novel off targets of baricitinib and tofacitinib by machine learning with a focus on thrombosis and viral infection. Scientific reports, 12(1), 7843.
FOLLOW US
Consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat. Ut wisi enim ad minim veniam, quis nostrud.
Go to Twitter
SHARING CODE
In the Molecular Machine learning team, we believe in the importance of open and reproducible research. Check out our GitHub repository for freely-available code!
Go To Github