Author

Lena Erlach

ETH ZurichORCID 0009-0002-0630-5764Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:machine learning (classical)· 2 Research Focus:benchmarking· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Organism:human· 1 Organism:mouse· 1 Research Focus:protein design· 1 Assay:protein sequence· 1

Frequent Co-Authors

Abhinav GuptaiD· 1 shared Bonnie BergeriD· 1 shared Brian C. MacknessiD· 1 shared Bryan D. BrysoniD· 1 shared Chiho Im· 1 shared Daniel Neumeier· 1 shared Derek M. MasoniD· 1 shared Maria WendtiD· 1 shared

Activity by Year

2024-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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1 shared paperBioinformatics

Abhinav GuptaORCID

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2 papers

Recent·Most cited

Antibody affinity engineering using antibody repertoire data and machine learning

Lena Erlach, Simon Friedensohn, Daniel Neumeier, Derek M. Mason, Sai T. Reddy

This study uses antibody repertoire data and machine learning to predict and engineer antibody affinities, achieving accurate predictions and successful in silico design of seven out of eight synthetic variants.

bioRxiv · Jan 10, 2025 · PreprintBioengineering

Learning the Language of Antibody Hypervariability

Rohit Singh, Chiho Im, Yu Qiu, Brian C. Mackness, Abhinav Gupta, Taylor Joren, Samuel Sledzieski, Lena Erlach, Maria Wendt, Yves Fomekong‐Nanfack, Bryan D. Bryson, Bonnie Berger

This paper introduces AbMAP, a transfer learning framework for antibody sequence analysis, showing it can predict structure, binding, and paratope with high accuracy and enable antibody optimization.

Proceedings of the National Academy of Sciences · Dec 30, 2024Peer-reviewedBioinformatics