Francesca Grisoni

According to our database, Francesca Grisoni authored at least 13 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

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

Davide Boldini, Davide Ballabio, Viviana Consonni, Roberto Todeschini, Francesca Grisoni, Stephan A. Sieber

J. Cheminformatics, December, 2024

2023

Practical guidelines for the use of gradient boosting for molecular property prediction.

Davide Boldini, Francesca Grisoni, Daniel Kuhn, Lukas Friedrich, Stephan A. Sieber

J. Cheminformatics, December, 2023

Correction to "Exposing the Limitations of Molecular Machine Learning with Activity Cliffs".

Derek van Tilborg, Alisa Alenicheva, Francesca Grisoni

J. Chem. Inf. Model., April, 2023

2022

Exposing the Limitations of Molecular Machine Learning with Activity Cliffs.

Derek van Tilborg, Alisa Alenicheva, Francesca Grisoni

J. Chem. Inf. Model., 2022

Perplexity-Based Molecule Ranking and Bias Estimation of Chemical Language Models.

Michael Moret, Francesca Grisoni, Paul Katzberger, Gisbert Schneider

J. Chem. Inf. Model., 2022

Structure-based drug discovery with deep learning.

Riza Özçelik, Derek van Tilborg, José Jiménez-Luna, Francesca Grisoni

CoRR, 2022

2021

Geometric deep learning on molecular representations.

Kenneth Atz, Francesca Grisoni, Gisbert Schneider

Nat. Mach. Intell., 2021

2020

Generative molecular design in low data regimes.

Michael Moret, Lukas Friedrich, Francesca Grisoni, Daniel Merk, Gisbert Schneider

Nat. Mach. Intell., 2020

Drug discovery with explainable artificial intelligence.

José Jiménez-Luna, Francesca Grisoni, Gisbert Schneider

Nat. Mach. Intell., 2020

Consensus versus Individual QSARs in Classification: Comparison on a Large-Scale Case Study.

Cecile Valsecchi, Francesca Grisoni, Viviana Consonni, Davide Ballabio

J. Chem. Inf. Model., 2020

Bidirectional Molecule Generation with Recurrent Neural Networks.

Francesca Grisoni, Michael Moret, Robin Lingwood, Gisbert Schneider

J. Chem. Inf. Model., 2020

2019

Machine Learning Consensus To Predict the Binding to the Androgen Receptor within the CoMPARA Project.

Francesca Grisoni, Viviana Consonni, Davide Ballabio

J. Chem. Inf. Model., 2019

2016

Beware of Unreliable <i>Q</i>²! A Comparative Study of Regression Metrics for Predictivity Assessment of QSAR Models.

Roberto Todeschini, Davide Ballabio, Francesca Grisoni

J. Chem. Inf. Model., 2016