Adrian Sanborn is cofounder and CEO of Endura Therapeutics. He combines computation and experimental biology, with research on genome organization and gene regulation, experience building RNA datasets at Atomic AI and current work on RNA-targeting drug discovery.[1][2]
Start with Foundries vs Navigators: Lowering the Cost of Science →
X Career
Sanborn earned a mathematics BA at Harvard, a flute-performance master’s at New England Conservatory and a computer-science PhD at Stanford, working in Roger Kornberg’s structural-biology lab. At Atomic AI, he helped establish bioscience research, RNA data collection and an early drug-discovery roadmap. He later cofounded Endura with Jeff Rogers and Ayah Abdeldayem.[1][2]
Contributions
Test a model of how the genome folds
Sanborn and Suhas Rao contributed equally to a study proposing loop extrusion as an explanation for chromosome loops and domains. The team combined physical modeling with Hi-C measurements and targeted genome editing. Predicting how changes to specific DNA sites would alter folding made the model experimentally testable, rather than just a description of existing contact maps.[4]
Measure the behavior of transcriptional activation domains
Sanborn is first and corresponding author, alongside corresponding author Roger Kornberg, of a study of activation domains: protein regions that help activate gene transcription. It examines their interaction with Mediator, a transcription-regulating protein complex. The team combined large-scale measurements with computational analysis to connect sequence features to activity and binding. The paper explains how varied sequences can support dynamic interactions, without requiring one fixed folded structure.[5]
Connect genomic knowledge to an RNA-drug research program
His Endura essay describes the company’s plan to use covalent chemistry and scalable sequencing to discover small molecules that interfere with RNA translation. It explains the scientific and delivery problems the team is trying to address. The work is a drug-discovery program; the essay’s proposed benefits describe its aims.[6]
Ideas
In Foundries vs Navigators, Sanborn distinguishes companies that industrialize physical measurements from companies that use AI to improve the decisions and software around research. His Endura examples include analysis, internal tools and choosing which experiments to run. He emphasizes that faster knowledge work does not itself remove the time and cost of a physical experiment.[3]
Tools, writing & talks
Writing, research & talks
Start here
A scientist-founder’s account of AI’s effects on experimental throughput and everyday research decisions.
Begin with the distinction between changing the experiment and changing the work around it. The essay is accessible without specialist biology knowledge; read the Endura examples as one company’s operating experience, then consider which bottleneck your own research faces.
Guest author[3]Original source- Chromatin extrusion explains key features of loop and domain formation in wild-type and engineered genomesaidenlab.org
A loop-extrusion model tested with genome-contact measurements and targeted mutations.
Equal contributor with Suhas Rao[4] - Simple biochemical features underlie transcriptional activation domain diversity and dynamic, fuzzy binding to Mediatorelifesciences.org
An experimental and computational study of activation-domain sequence, function and binding.
First and corresponding author[5] - The Genomics Revolution Is Only Half Finishedenduratx.com
Sanborn’s explanation of the RNA-targeting research program behind Endura.
Author[6]