T-REX: open-source LLM agent controller orchestrates de novo protein binder design

ArashVahdat · x · 2026-09-25

The Stanford ml-struct-bio team (first author Minkyu Jeon, amplified by Arash Vahdat) released its first protein design paper, T-REX (Target-adaptive Rescue-Explore-eXploit): an agentic campaign controller for high-throughput de novo protein binder design, fully open source with a preprint.

Key ideas:

Frames protein design as an online allocation problem over heterogeneous tools (Proteina-complexa, Boltzgen, Bindcraft, etc.), with agents reasoning over which tools and settings to run;
Planner and Supervisor LLMs propose and prioritize jobs while deterministic code validates them and manages generation, redesign, and evaluation—efficiently saturating GPUs;
A granular agent taxonomy: Rescue / Explore / eXploit roles;
Campaigns need only four user choices (target, GPUs per node, duration, output dir) via trex setup -> init -> check -> submit;
Verified on Linux x8664 with NVIDIA H100 GPUs under Slurm.

A solid AI4Science case of LLM agents acting as lab experiment orchestrators.

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T-REX: open-source LLM agent controller orchestrates de novo protein binder design

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