F
Francesca Grisoni
Researcher at ETH Zurich
Publications - 83
Citations - 2814
Francesca Grisoni is an academic researcher from ETH Zurich. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 19, co-authored 72 publications receiving 1505 citations. Previous affiliations of Francesca Grisoni include University of Milano-Bicocca & University of Milan.
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Drug discovery with explainable artificial intelligence
TL;DR: A review of the most prominent algorithmic concepts of explainable artificial intelligence, and forecasts future opportunities, potential applications as well as several remaining challenges is provided in this article. But, the review is limited to the use of deep learning for drug discovery.
Journal ArticleDOI
CERAPP: Collaborative Estrogen Receptor Activity Prediction Project
Kamel Mansouri,Ahmed Abdelaziz,Aleksandra Rybacka,Alessandra Roncaglioni,Alexander Tropsha,Alexandre Varnek,Alexey V. Zakharov,Andrew Worth,Ann M. Richard,Christopher M. Grulke,Daniela Trisciuzzi,Denis Fourches,Dragos Horvath,Emilio Benfenati,Eugene N. Muratov,Eva Bay Wedebye,Francesca Grisoni,Giuseppe Felice Mangiatordi,Giuseppina M. Incisivo,Huixiao Hong,Hui W. Ng,Igor V. Tetko,Ilya A. Balabin,Jayaram Kancherla,Jie Shen,Julien Burton,Marc C. Nicklaus,Matteo Cassotti,Nikolai Georgiev Nikolov,Orazio Nicolotti,Patrik L. Andersson,Qingda Zang,Regina Politi,Richard D. Beger,Roberto Todeschini,Ruili Huang,Sherif Farag,Sine Abildgaard Rosenberg,Svetoslav Slavov,Xin Hu,Richard S. Judson +40 more
TL;DR: This project demonstrated the possibility to screen large libraries of chemicals using a consensus of different in silico approaches and the efficacy of using predictive computational models trained on high-throughput screening data to evaluate thousands of chemicals for ER-related activity and prioritize them for further testing.
Journal ArticleDOI
De Novo Design of Bioactive Small Molecules by Artificial Intelligence
TL;DR: This study presents the first‐time prospective application of a deep learning model for designing new druglike compounds with desired activities and synthesized five top‐ranking compounds designed by the generative model.
Posted Content
Drug discovery with explainable artificial intelligence
TL;DR: This review summarizes the most prominent algorithmic concepts of explainable artificial intelligence, and dares a forecast of the future opportunities, potential applications, and remaining challenges.
Journal ArticleDOI
Multivariate comparison of classification performance measures
TL;DR: In this study, different global measures of classification performances are compared by means of results achieved on an extended set of real multivariate datasets and a set of benchmark values based on different random classification scenarios are introduced.