Author

Nathalie Basdevant

Centre National de la Recherche ScientifiqueORCID 0000-0002-6775-2907Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Approach:deep learning· 1 Research Focus:protein folding & dynamics· 1 Assay:protein structure· 1 Research Focus:software / tool· 1 Approach:transformer / foundation model· 1

Frequent Co-Authors

Alaa RegueiiD· 1 shared Antoine TalyiD· 1 shared Beatrice CavigliaiD· 1 shared Carine TisnéiD· 1 shared Chantal PrévostiD· 1 shared Élise Duboué-DijoniD· 1 shared Fabrice LeclerciD· 1 shared JianJun Tao· 1 shared

Activity by Year

2026-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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1 shared paperBiophysics

Alaa RegueiORCID

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1 paper

Recent·Most cited

Deep-learning predictions of biomolecular structures : persistent limitations and new horizons extended by explicit ion addition

Jules Marien, Sujith Sritharan, Beatrice Caviglia, Raphaëlle Versini, Lucas Auclair, Pierre Barraud, Carine Tisné, Alaa Reguei, Samuel Murail, Fabrice Leclerc, Élise Duboué-Dijon, JianJun Tao, Nathalie Basdevant, Marc Baaden, Chantal Prévost, , Antoine Taly

This paper evaluates limitations of deep learning in biomolecular structure prediction, highlighting the benefits of explicit ion modeling for improving conformational accuracy.

bioRxiv · Jul 26, 2026 · PreprintBiophysics