Francesca Grisoni

Orcid: 0000-0001-8552-6615

Affiliations:
  • Eindhoven University of Technology, The Netherlands


According to our database1, Francesca Grisoni authored at least 16 papers between 2016 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Effectiveness of molecular fingerprints for exploring the chemical space of natural products.
J. Cheminformatics, December, 2024

Traversing chemical space with active deep learning for low-data drug discovery.
Nat. Comput. Sci., October, 2024

A Hitchhiker's Guide to Deep Chemical Language Processing for Bioactivity Prediction.
CoRR, 2024

Predicting Metabolic Reactions with a Molecular Transformer for Drug Design Optimization.
Proceedings of the IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 2024

2023
Practical guidelines for the use of gradient boosting for molecular property prediction.
J. Cheminformatics, December, 2023

Correction to "Exposing the Limitations of Molecular Machine Learning with Activity Cliffs".
J. Chem. Inf. Model., April, 2023

2022
Exposing the Limitations of Molecular Machine Learning with Activity Cliffs.
J. Chem. Inf. Model., 2022

Perplexity-Based Molecule Ranking and Bias Estimation of Chemical Language Models.
J. Chem. Inf. Model., 2022

Structure-based drug discovery with deep learning.
CoRR, 2022

2021
Geometric deep learning on molecular representations.
Nat. Mach. Intell., 2021

2020
Generative molecular design in low data regimes.
Nat. Mach. Intell., 2020

Drug discovery with explainable artificial intelligence.
Nat. Mach. Intell., 2020

Consensus versus Individual QSARs in Classification: Comparison on a Large-Scale Case Study.
J. Chem. Inf. Model., 2020

Bidirectional Molecule Generation with Recurrent Neural Networks.
J. Chem. Inf. Model., 2020

2019
Machine Learning Consensus To Predict the Binding to the Androgen Receptor within the CoMPARA Project.
J. Chem. Inf. Model., 2019

2016
Beware of Unreliable <i>Q</i><sup>2</sup>! A Comparative Study of Regression Metrics for Predictivity Assessment of QSAR Models.
J. Chem. Inf. Model., 2016


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