Author

Steven T. Rutherford

GenentechORCID 0000-0002-4758-4248Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:bacteria (other)· 2 Research Focus:drug response / drug discovery· 2 Research Focus:benchmarking· 1 Approach:contrastive learning· 1 Approach:deep learning· 1 Approach:experimental assay· 1 Research Focus:host-pathogen & immune response· 1 Approach:statistical model· 1

Frequent Co-Authors

Gabriele ScaliaiD· 2 shared Kerry R. BuchholziD· 2 shared Nathaniel DiamantiD· 2 shared Nicholas J. SkeltoniD· 2 shared Tommaso BiancalaniiD· 2 shared Anh Miu· 1 shared Aviv RegeviD· 1 shared Elizabeth Skippington· 1 shared

Activity by Year

2024-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 41 mutual co-author links

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2 shared papersBioinformatics

Gabriele ScaliaORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Independent tuning of outer membrane fluidity and mechanics in Gram-negative bacteria

Jiawei Sun, Gvantsa Gutishvili, You He, Ryan Adam Valdez, Handuo Shi, Petra Anne Levin, Thomas J. Silhavy, James C. Gumbart, Steven T. Rutherford, Kerwyn Casey Huang

This study shows that outer membrane fluidity and mechanics in Gram-negative bacteria can be independently controlled by altering lipid composition and crosslinking.

bioRxiv · Sep 19, 2026 · PreprintMicrobiology

TxConformal: Controlling False Discoveries in AI-Driven Therapeutic Discovery

Ying Jin, Kexin Huang, Nathaniel Diamant, Kerry R. Buchholz, Steven T. Rutherford, Nicholas J. Skelton, Tommaso Biancalani, Gabriele Scalia, Jure Leskovec, Emmanuel J. Candès

This paper introduces TxConformal, a framework that controls false discoveries in AI-driven therapeutic discovery by adjusting for distribution shifts and providing confidence measures for AI scores.

bioRxiv · Apr 30, 2026 · PreprintBioinformatics

A high-throughput phenotypic screen combined with an ultra-large-scale deep learning-based virtual screening reveals novel scaffolds of antibacterial compounds

Gabriele Scalia, Steven T. Rutherford, Ziqing Lu, Kerry R. Buchholz, Nicholas J. Skelton, Kangway V. Chuang, Nathaniel Diamant, Jan-Christian Hütter, Jerome-Maxim Luescher, Anh Miu, Jeff Blaney, Leo Gendelev, Elizabeth Skippington, Gregory J. Zynda, Nia Dickson, , Tommaso Biancalani

This study combines high-throughput screening with deep learning to identify novel antibacterial compounds, achieving a 90X higher hit rate than traditional methods.

bioRxiv · Sep 11, 2024 · PreprintBioinformatics