Author

Ilyas Grandguillaume

InsermORCID 0000-0002-1113-8289Unclaimed 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:database / resource· 1 Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1 Assay:protein structure· 1

Frequent Co-Authors

Catherine EtchebestiD· 2 shared Fernando Luís Barroso da SilvaiD· 2 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

2 top collaborators · 1 mutual co-author link

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2 shared papersBioinformatics

Catherine EtchebestORCID

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2 papers

Recent·Most cited

BioinformaticsJournal of Chemical Information and ModelingPeer-reviewedJul 27, 2026

ARID-sf: A physics-informed Deep Learning scoring function to improve Antibody-Antigen docking model ranking

Ilyas Grandguillaume, Fernando Luís Barroso da Silva, Catherine Etchebest

ARID-sf improves antibody-antigen docking model ranking by combining force fields, structural features, and embeddings in a deep learning framework, outperforming existing methods across diverse scenarios.

DOI 10.1021/acs.jcim.6c00836

deep learningdrug response / drug discovery

𝕏 PostBluesky

BioinformaticsJournal of Chemical Information and ModelingPeer-reviewedOct 17, 2025

ANABAG: Annotated Antibody Antigen dataset with unique features for Antibody Engineering Applications

Ilyas Grandguillaume, Fernando Luís Barroso da Silva, Catherine Etchebest

ANABAG provides a curated dataset of antibody-antigen complexes with structural and physicochemical features to improve Ab-Ag modeling and antibody engineering.

4 citations

protein structuredatabase / resource

𝕏 PostBluesky