Gyu Rie Lee

Also known as Gyuri Lee, Gyurie Lee

Gyu Rie Lee is a computational protein-design researcher who builds deep-learning methods for generating enzymes, small-molecule binders and biosensors. After earning a BS in 2011 and a PhD in 2017 at Seoul National University, she worked in David Baker's laboratory at the University of Washington and the Howard Hughes Medical Institute from 2018 to 2025, coauthoring deep-learning studies on artificial luciferases in February 2023, RoseTTAFold All-Atom in 2024 and LigandMPNN in March 2025. In February 2025 she joined KAIST to lead the Protein Design Laboratory, and in March 2026 she was first author of a Nature Communications study that designed binders for six small-molecule targets and a cortisol biosensor. She is an assistant professor in the Department of Biological Sciences at KAIST, an adjunct professor at the KAIST AI College, and a mentor professor in the AI-CRED Innovative Drug Research Group of the InnoCORE project.

Now at: KAIST

Timeline

2011–2018

Degrees and postdoc at Seoul National

She earns a BS in 2011 and completes a PhD in 2017 at Seoul National University. From 2017 to 2018, she works as a postdoctoral fellow at Seoul National University.
2018–2025

Postdoc and staff in Baker's lab

From 2018 to 2024, Lee works as a postdoctoral researcher and staff scientist in David Baker's laboratory at the University of Washington and the Howard Hughes Medical Institute. Her Seattle appointments span postdoctoral scholar from 2018 to 2022, acting instructor from 2022 to 2023, and research specialist at the University of Washington and HHMI from 2023 to 2025.
February 2023

Luciferases designed with deep learning

In February 2023, she coauthors "De novo design of luciferases using deep learning" in Nature. The study introduces a deep-learning family-wide hallucination method that generates idealized protein structures with diverse pocket shapes and designed sequences to create artificial luciferases.
2024

Biomolecular assemblies with RoseTTAFold All-Atom

In 2024, she coauthors the Science paper presenting RoseTTAFold All-Atom (RFAA). The AI network predicts biomolecular assemblies containing proteins, nucleic acids, small molecules, metals and chemical modifications.
February 2025

Protein Design Laboratory at KAIST

She joins KAIST in February 2025 as an assistant professor in the Department of Biological Sciences and leads the Protein Design Laboratory. She also serves as a mentor professor in the AI-CRED Innovative Drug Research Group of the InnoCORE project, researching enzyme and peptide drug design.
March 2025

The LigandMPNN sequence design method

In March 2025, Lee is second author of "Atomic context-conditioned protein sequence design using LigandMPNN" in Nature Methods. LigandMPNN is a deep-learning protein sequence design method that explicitly models all nonprotein components of biomolecular systems.
March 2026

Designed binders and a cortisol sensor

In March 2026, she is first author of "Small-molecule binding and sensing with a designed protein family" in Nature Communications, with David Baker as corresponding author. The team combines deep learning and physics-based methods to generate proteins with designable pocket geometries, designing binders for six small-molecule targets and building an AI-designed cortisol biosensor.
As of October 2026

Two faculty posts at KAIST

Her KAIST appointment as assistant professor in the Department of Biological Sciences runs from 2025 to the present. She also serves as an adjunct professor at the KAIST AI College, where her core AI competencies include generative AI and inverse design, data-centric AI, foundation models, and reinforcement learning and control.

Career

Since 2025KAISTAssistant professor
2018–2024University of WashingtonPostdoctoral researcher and staff scientist
2018–2024Howard Hughes Medical InstitutePostdoctoral researcher and staff scientist