Author

Saeed Saremi

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

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Bioengineering· 1 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Approach:machine learning (classical)· 2 Research Focus:drug response / drug discovery· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Nathan C. FreyiD· 2 shared Richard BonneauiD· 2 shared Ryan L. KellyiD· 2 shared Simon P. KelowiD· 2 shared Alicja CzubatyiD· 1 shared Allen GoodmaniD· 1 shared Amy WangiD· 1 shared Andreas LoukasiD· 1 shared

Activity by Year

2025-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersBioinformatics

Nathan C. FreyORCID

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Author profile →

2 papers

Recent·Most cited

Deep Evolutionary Fitness Inference for Variant Nomination from Directed Evolution

Max W. Shen, Nathaniel Diamant, Christina Helmling, Raymond Newland, Ziqing Lu, Clara Fannjiang, Simon P. Kelow, Nathan C. Frey, Saeed Saremi, Ryan L. Kelly, Richard Bonneau, Gabriele Scalia, Christian N. Cunningham, Tommaso Biancalani

This paper introduces EVFI and DeepEVFI, machine learning methods that infer variant fitness from directed evolution data, identifying tighter binders missed by conventional approaches.

bioRxiv · Jul 22, 2025 · PreprintBioinformatics

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