Author

Louis Robinson

ORCID 0000-0002-0076-6212Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:protein sequence· 3 Research Focus:protein structure prediction· 2 Approach:Bayesian inference· 1 Research Focus:benchmarking· 1 Approach:contrastive learning· 1 Approach:deep learning· 1 Research Focus:sequence-to-function modeling· 1 Research Focus:software / tool· 1

Frequent Co-Authors

Thomas D. BarrettiD· 3 shared Liviu CopoiuiD· 2 shared Timothy J. AtkinsoniD· 2 shared Alex Graves· 1 shared Alexandre Laterre· 1 shared Amelia Villegas-MorcilloiD· 1 shared Arthur FlajoletiD· 1 shared Benoit Gaujac· 1 shared

Activity by Year

2022-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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3 shared papersBioinformatics

Thomas D. BarrettORCID

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3 papers

Recent·Most cited

Protein Sequence Modelling with Bayesian Flow Networks

Timothy J. Atkinson, Thomas D. Barrett, Scott Cameron, Bora Guloglu, Matthew Greenig, Charlie B. Tan, Louis Robinson, Alex Graves, Liviu Copoiu, Alexandre Laterre

This paper introduces ProtBFN, a Bayesian Flow Network for protein sequence generation, which outperforms existing models and is fine-tuned for antibody generation.

Nature Communications · Apr 3, 2025Peer-reviewedBioinformatics

Contrasting Sequence with Structure: Pre-training Graph Representations with PLMs

Louis Robinson, Timothy J. Atkinson, Liviu Copoiu, Patrick Bordes, Thomas Pierrot, Thomas D. Barrett

The paper introduces BioCLIP, a contrastive learning framework for pre-training Protein Structure Models using Protein Language Models, achieving performance comparable to specialized methods on various tasks.

bioRxiv · Dec 1, 2023 · PreprintBioinformatics

So ManyFolds, So Little Time: Efficient Protein Structure Prediction With pLMs and MSAs

Thomas D. Barrett, Amelia Villegas-Morcillo, Louis Robinson, Benoit Gaujac, David Adméte, Elia Saquand, Karim Beguir, Arthur Flajolet

The paper presents a streamlined AlphaFold variant using pLMs and MSAs, showing that combining or using either modality achieves comparable performance to models trained exclusively on one.

bioRxiv · Oct 15, 2022 · PreprintBioinformatics