Author

Constantin Schneider

University of OxfordORCID 0000-0002-2579-0307Unclaimed 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 structure· 3 Research Focus:benchmarking· 2 Research Focus:drug response / drug discovery· 2 Approach:machine learning (classical)· 2 Research Focus:protein structure prediction· 2 Approach:deep learning· 1 Approach:experimental assay· 1 Approach:graph neural network· 1

Frequent Co-Authors

Charlotte Mary DeaneiD· 4 shared Daniel CuttingiD· 2 shared Aleksandr KovaltsukiD· 1 shared Alexandre M. J. J. BonviniD· 1 shared Alissa M. HummeriD· 1 shared Andrew G. BuchananiD· 1 shared Bruck TaddeseiD· 1 shared Claire MarksiD· 1 shared

Activity by Year

2021-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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4 shared papersBioinformatics

Charlotte Mary DeaneORCID

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4 papers

Recent·Most cited

Investigating the Volume and Diversity of Data Needed for Generalizable Antibody-Antigen ΔΔG Prediction

Alissa M. Hummer, Constantin Schneider, Lewis Chinery, Charlotte Mary Deane

The paper evaluates the need for more diverse and larger datasets to improve antibody-antigen ΔΔG prediction using Graphinity, showing current experimental data is insufficient.

Nature Computational Science · Jul 8, 2025Peer-reviewedBioinformatics

Computational design of developable therapeutic antibodies: efficient traversal of binder landscapes and rescue of escape mutations

Frédéric A. Dreyer, Constantin Schneider, Aleksandr Kovaltsuk, Daniel Cutting, Matthew J. Byrne, Daniel Allen Nissley, Newton Wahome, Henry Kenlay, Claire Marks, David Errington, Richard J. Gildea, David Damerell, Pedro A. G. Tizei, Wilawan Bunjobpol, John F. Darby, , Charlotte Mary Deane

This paper presents a computational pipeline for designing therapeutic antibodies using AI and physics-based methods, showing improved binding and developability across SARS-CoV-2 variants.

bioRxiv · Oct 3, 2024 · PreprintBioinformatics

Towards the accurate modelling of antibody-antigen complexes from sequence using machine learning and information-driven docking

Marco Giulini, Constantin Schneider, Daniel Cutting, Nikita Desai, Charlotte Mary Deane, Alexandre M. J. J. Bonvin

The paper evaluates how machine learning-generated antibody and antigen structures can improve antibody-antigen complex modelling using HADDOCK, showing improved accuracy over ZDOCK.

Bioinformatics · Sep 27, 2024Peer-reviewedBioinformatics

DLAB - Deep learning methods for structure-based virtual screening of antibodies

Constantin Schneider, Andrew G. Buchanan, Bruck Taddese, Charlotte Mary Deane

DLAB uses deep learning to improve antibody-antigen docking and virtual screening by predicting binding and ranking poses for antibody drug candidates.

Bioinformatics · Sep 15, 2021Peer-reviewedBioinformatics