Author

Fabrice Leclerc

Centre National de la Recherche ScientifiqueORCID 0000-0002-5641-1525Unclaimed ORCID profile · follow to get notified of new preprints

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Fabrice Leclerc2 papers

Biology Disciplines

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Bioinformatics· 1 Biophysics· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1 Research Focus:protein folding & dynamics· 1 Assay:protein structure· 1 Research Focus:software / tool· 1 Approach:statistical model· 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 JianJun Tao· 1 shared Jules MarieniD· 1 shared

Activity by Year

2021-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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1 shared paperBiophysics

Alaa RegueiORCID

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2 papers

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

MCSS-based Predictions of Binding Mode and Selectivity of Nucleotide Ligands

Roy González‐Alemán, Nicolas Chevrollier, Manuel Simoes, Luís A. Montero, Fabrice Leclerc

The paper evaluates an MCSS-based docking approach for predicting binding modes and selectivity of nucleotide ligands, showing improved performance with hybrid solvent models and pose clustering.

Journal of Chemical Theory and Computation · Mar 25, 2021Peer-reviewedBioinformatics