arXiv cs.AI· Zachary Cutts, John Coller, Rachel Chen, Hua Tang, Mathias Bellaiche, Ryder Easterlin, Dina Zverinski, Petar Sirkovic, Avinatan Hassidim, Yossi Matias, Pooja Rao, Annalisa Pawlosky·· 12 小时前AI 评分57
适配体作为闭环实验室智能体分子发现的试验场
Aptamers as a sandbox for lab-in-the-loop agentic molecular discovery
AI 导读
研究以蛋白结合适配体设计为试验场，让基于 LLM 的推理智能体与湿实验形成闭环，针对 α-thrombin 和 myoglobin 完成三轮设计-测试-改进，每轮约 1000 个候选、共约 4400 个构建体。
正文 · 原文
Authors:Zachary Cutts, John Coller, Rachel Chen, Hua Tang, Mathias Bellaiche, Ryder Easterlin, Dina Zverinski, Petar Sirkovic, Avinatan Hassidim, Yossi Matias, Pooja Rao, Annalisa Pawlosky
Abstract: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 $\alpha$-thrombin and myoglobin across three design-test-improve cycles ($\sim$1,000 candidates per round; $\sim$4,400 constructs). Statistically significant, bead-corrected on-target hit rates among new candidates rose across rounds from 0.0% in Round 1 to 26.0% in Round 2 and 42.1% in Round 3 on $\alpha$-thrombin (37.1% excluding literature motif descendants), and from 10.2% in Round 1 to 3.7% in Round 2 and 33.5% in Round 3 on myoglobin. Single-clone bead-binding titrations of eight full-length $\alpha$-thrombin candidates confirmed concordance with pooled sequencing enrichment (Pearson $r = 0.90$). In these assays, literature-derived motifs engineered by the agents to reduce off-target reactivity in the pooled screen reached apparent affinities of $K_D = 24.6$--$133$ nM, whereas de novo scaffolds showed low-micromolar binding ($K_D > 1\ \mu$M).
We also report how our pipelines used myoglobin binding data to exploit the G-quadruplex binder space, diagnose cross-reactivity as on-target specificity dropped from 75.0% to 19.1%, and recover by developing strategies that restored specificity to 72.4%. We demonstrate that empirical lab data is more valuable than existing computational proxies, showing how iterative experimental feedback enables AI agents to diagnose assay artifacts and refine molecular designs across rounds.
| Comments: | Equal leadership (co-senior authors): Pooja Rao and Annalisa Pawlosky. 34 pages, 4 main figures, 1 main table, 3 supplementary figures, 4 supplementary tables |
| Subjects: | Biomolecules (q-bio.BM); Artificial Intelligence (cs.AI); Quantitative Methods (q-bio.QM) |
| Cite as: | arXiv:2610.24806 [q-bio.BM] |
| (or arXiv:2610.24806v1 [q-bio.BM] for this version) | |
| https://doi.org/10.48550/arXiv.2610.24806 arXiv-issued DOI via DataCite |
Submission history
From: Pooja Rao [view email]
[v1]
Sat, 17 Oct 2026 18:21:50 UTC (7,897 KB)
来源：arXiv cs.AI · arxiv.org