Author

Geraldene Munsamy

University of OxfordORCID 0000-0002-0619-2109Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Approach:deep learning· 3 Research Focus:benchmarking· 2 Assay:metagenomics· 2 Research Focus:software / tool· 2 Approach:statistical model· 2 Research Focus:database / resource· 1 Assay:DNA methylation· 1 Research Focus:drug response / drug discovery· 1

Frequent Co-Authors

Gavin Ayres· 2 shared Noelia FerruziD· 2 shared Philipp LorenziD· 2 shared Tanggis Bohnuud· 2 shared Aaron W. KollaschiD· 1 shared Alexandros Papadopoulos· 1 shared Ali Taghibakhshi· 1 shared Alicia Frame· 1 shared

Activity by Year

2024-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 39 mutual co-author links

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2 shared papersGenomics

Gavin Ayres

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Author profile →

3 papers

Recent·Most cited

Designing AI-programmable therapeutics with the EDEN family of foundation models

Geraldene Munsamy, Gavin Ayres, Carla Greco, Keith Y K Kam, Gus Minto-Cowcher, John St John, Tanggis Bohnuud, Matthew H. Bakalar, William Chow, Robert J. Pecoraro, Marcelo D. T. Torres, Aaron W. Kollasch, Marcus H. Y. Leung, Hassan Sirelkhatim, Francesco Farina, , Philipp Lorenz

This paper introduces the EDEN family of foundation models for AI-programmable therapeutic design, demonstrating their ability to generate functional gene insertions, antibiotic peptides, and synthetic microbiomes.

bioRxiv · Feb 9, 2026 · PreprintGenomics

Conditional language models enable the efficient design of proficient enzymes

Geraldene Munsamy, R. Illanes, Silvia Fruncillo, Ioanna T. Nakou, Sebastian Lindner, Gavin Ayres, Lesley S. Sheehan, Steven J. Moss, Ulrich Eckhard, Philipp Lorenz, Noelia Ferruz

An enzyme-specific conditional language model generates active carbonic anhydrases and lactate dehydrogenases to user specification, some under 40% identity to natural proteins, with no retraining.

bioRxiv · May 3, 2024 · PreprintBioinformatics

IMPROVING ALPHAFOLD2 PERFORMANCE WITH A GLOBAL METAGENOMIC & BIOLOGICAL DATA SUPPLY CHAIN

Geraldene Munsamy, Tanggis Bohnuud, Philipp Lorenz

The paper improves AlphaFold2 performance using a global metagenomic data supply chain, achieving better protein structure predictions on CASP15 and CAMEO targets.

bioRxiv · Mar 6, 2024 · PreprintGenomics