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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

Indonesian

Terlepas dari kemajuan yang diaktifkan dalam pengembangan data molekuler yang didorong oleh AI, pipa penemuan yang ada pada scaffolding beroperasi sebagian besar di silico, dengan umpan balik laboratorium basah dikhususkan untuk validasi daripada peningkatan iteratif. Apakah model bahasa yang lebih spesifik (LLM)-berbasis pertimbangan agen khusus dapat mempercepat penemuan de novo dengan iterasi dengan data laboratorium tetap tidak terbukti. Di sini kita menggunakan desain aptamer yang mengikat protein-microbe sebagai siklus sandbox (1-2 minggu, \$1-\$4,400 per kandidat) untuk menunjukkan laboratorium basah dalam lingkaran tertutup dengan sistem AI. Kami melakukan tugas agentic pipa dengan pengembangan aptamer pengikat terhadap $α$-thrombin dan myoglobin yang digunakan dalam tiga siklus diagnostik-test-prove ($1,000 dro

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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