Grisoni, Francesca

Drug discovery with explainable artificial intelligence

Jiménez-Luna, J., Grisoni, F., & Schneider, G. (2020) · Nature Machine Intelligence

Explainable Artificial Intelligence (XAI)Computational Drug Discovery Methods

Cited by 1,063

Artificial intelligence in drug discovery: recent advances and future perspectives

Jiménez-Luna, J., Grisoni, F., Weskamp, N., & Schneider, G. (2021) · Expert Opinion on Drug Discovery

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 464

CERAPP: Collaborative Estrogen Receptor Activity Prediction Project

Mansouri, K., Abdelaziz, A., Rybacka, A., Roncaglioni, A., Tropsha, A., Varnek, A., Zakharov, A., Worth, A., Richard, A. M., Grulke, C. M., et al. (2016) · Environmental Health Perspectives

Computational Drug Discovery MethodsEffects and risks of endocrine disrupting chemicals

Cited by 375

De Novo Design of Bioactive Small Molecules by Artificial Intelligence

Merk, D., Friedrich, L., Grisoni, F., & Schneider, G. (2018) · Molecular Informatics

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 373

Exposing the limitations of molecular machine learning with activity cliffs.

van Tilborg, D., Alenicheva, A., & Grisoni, F. (2022) · Journal of Chemical Information and Modeling

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 340

Exposing the limitations of molecular machine learning with activity cliffs.

van Tilborg, D., Alenicheva, A., & Grisoni, F. (2022) · Journal of Chemical Information and Modeling

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 339

Exposing the limitations of molecular machine learning with activity cliffs.

van Tilborg, D., Alenicheva, A., & Grisoni, F. (2022) · Journal of Chemical Information and Modeling

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 337

Exposing the Limitations of Molecular Machine Learning with Activity Cliffs

van Tilborg, D., Alenicheva, A., & Grisoni, F. (2022) · Journal of Chemical Information and Modeling

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 337

Generative molecular design in low data regimes

Moret, M., Friedrich, L., Grisoni, F., Merk, D., & Schneider, G. (2020) · Nature Machine Intelligence

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 219

Combining generative artificial intelligence and on-chip synthesis for de novo drug design

Grisoni, F., Huisman, B. J. H., Button, A. L., Moret, M., Atz, K., Merk, D., & Schneider, G. (2021) · Science Advances

Computational Drug Discovery MethodsInnovative Microfluidic and Catalytic Techniques Innovation

Cited by 190

Effectiveness of molecular fingerprints for exploring the chemical space of natural products

Boldini, D., Ballabio, D., Consonni, V., Todeschini, R., Grisoni, F., & Sieber, S. A. (2024) · Journal of Cheminformatics

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 97

Deep learning for low-data drug discovery: Hurdles and opportunities

van Tilborg, D., Brinkmann, H., Criscuolo, E., Rossen, L., Özçelik, R., & Grisoni, F. (2024) · Current Opinion in Structural Biology

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 61