Author

Taylor Joren

Genentech

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

Click to filter author papers

Bioengineering· 2 Bioinformatics· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

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

Frequent Co-Authors

Allen GoodmaniD· 2 shared Kyunghyun ChoiD· 2 shared Nathan C. FreyiD· 2 shared Richard BonneauiD· 2 shared Vladimir GligorijevićiD· 2 shared Abhinav GuptaiD· 1 shared Alicja CzubatyiD· 1 shared Amy WangiD· 1 shared

Activity by Year

2024-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersBioengineering

Allen GoodmanORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Lab-in-the-loop therapeutic antibody design with deep learning

Nathan C. Frey, Isidro Hötzel, Samuel Stanton, Ryan L. Kelly, Robert G. Alberstein, Emily K. Makowski, Karolis Martinkus, Daniel Berenberg, Jack J. Bevers, Tyler Bryson, Pamela Chan, Yongmei Chen, Alicja Czubaty, Tamica D’Souza, Henri Dwyer, , Vladimir Gligorijević

This paper introduces a machine learning framework for therapeutic antibody design that integrates computational methods with experimental validation, resulting in significantly improved binding affinity for multiple targets.

bioRxiv · Feb 19, 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

Cramming Protein Language Model Training in 24 GPU Hours

Nathan C. Frey, Taylor Joren, Aya Abdelsalam Ismail, Allen Goodman, Richard Bonneau, Kyunghyun Cho, Vladimir Gligorijević

This paper trains a protein language model in 24 GPU hours, achieving performance comparable to a much larger model trained for 15,000x more GPU hours.

bioRxiv · May 14, 2024 · PreprintBioengineering