Author

Davide Rigoni

University of PaduaORCID 0000-0003-2092-3577Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Research Focus:drug response / drug discovery· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Research Focus:perturbation response prediction· 1 Research Focus:sequence-to-function modeling· 1 Approach:transformer / foundation model· 1

Frequent Co-Authors

Cristian TaccioliiD· 2 shared Alessandro SperdutiiD· 1 shared Dario RighelliiD· 1 shared Fabio BoveiD· 1 shared Giuseppe MangioniiD· 1 shared Marco GrassiaiD· 1 shared Marilena Valeria IorioiD· 1 shared Nicoletta BianchiiD· 1 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 31 mutual co-author links

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2 shared papersBioinformatics

Cristian TaccioliORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintSep 18, 2026

LLMsFold: Integrating Large Language Models and Biophysical Simulations for De Novo Drug Design

Wageesha Widuranga, Davide Rigoni, Fabio Bove…·Cristian Taccioli

LLMsFold designs and validates new small molecules targeting pathogenic proteins using LLMs and biophysical simulations, showing strong in silico binding and drug-like profiles.

DOI 10.64898/2026.03.02.709055

deep learningtransformer / foundation model

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BioinformaticsInternational Journal of Molecular SciencesPeer-reviewedJun 4, 2024

TumFlow: An AI Model for Predicting New Anticancer Molecules

Davide Rigoni, Sachithra Kalhari Yaddehige, Nicoletta Bianchi…·Cristian Taccioli

TumFlow generates and optimizes new anticancer molecules using AI, showing improved synthetic feasibility and predicted efficacy against melanoma.

6 citations

humanmachine learning (classical)

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