Newest papers

Aptamers as a sandbox for lab-in-the-loop agentic molecular discovery

Zachary Cutts, John Coller, Rachel Chen, Hua Tang, Mathias Bellaiche, Ryder Easterlin, Dina Zverinski, Petar Sirkovic, Avinatan Hassidim, Yossi Matias, Pooja Rao, Annalisa Pawlosky · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.24806 · cited by 0 works (OpenAlex) · open-access PDF

Korean

그것은 재활성화 된 데이터 실험실 생성에 대한 구체적인 혁신이 있음에도 불구하고, 기존의 발견 파이프 라인은 실리코에서 주로 작동하고, 젖은 실험실 피드백은 iterative 개선 대신 검증을 위해 예비되어 있습니다. 대형 언어 모델 (LLM) 기반의 구체적 인 추론 에이전트가 실험실 데이터를 반복하여 발견을 가속화 할 수 있는지 여부는 여전히 증명되지 않습니다. 여기에 우리는 단백질 묶는 파이프 라인 디자이너 디자인을 샌드박스 (1-2 주 사이클, $ 1 ~ $ 3 ~ $ 4,000 개)로 사용하여 AI 시스템과 함께 닫힌 루프에서 젖은 실험실을 보여줍니다. 우리는 $ 1 ~ $ 1의 디자이

Machine translation (Meta M2M100); the English original follows.

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 $α$-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 $α$-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 $α$-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 ($K_D > 1\ μ$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.

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Topics: Aptamer, Sandbox (software development), Computational biology, Computer science, In silico, Binding affinities

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