Aptamers as a sandbox for lab-in-the-loop agentic molecular discovery
Despite breakthroughs in AI-driven molecular generation, existing discovery pipelines operate largely in silico, with wet-lab feedback reserved for validation rather than iterative improvement. Whether large language model (LLM)-based reasoning agents can accelerate de novo discovery by iterating with lab data remains unproven. Here we use protein-binding aptamer design as a sandbox (1-2 week cycles, \$1-\$3 per candidate) to demonstrate a wet lab in a closed loop with AI systems. We tasked agentic pipelines with developing aptamer binders against $α$-thrombin and myoglobin across three design-test-improve cycles ($\sim$1,000 candidates per round; $\sim$4,400 constructs). Statistically si...
| Authors | Zachary Cutts, John Coller, Rachel Chen et al. |
|---|---|
| Primary category | q-bio.BM |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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