Aptamers as a sandbox for lab-in-the-loop agentic molecular discovery
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper demonstrates that LLM-based agentic pipelines can effectively iterate with wet-lab data to improve molecular design, achieving significant hit rates and nanomolar binders in a closed-loop aptamer discovery campaign. By rigorously benchmarking against computational predictors and showing their inadequacy, the study establishes the necessity of empirical feedback loops for AI-driven discovery in data-scarce biological domains, offering a robust framework for future self-driving laboratory systems.
Methodology
The paper proposes a closed-loop framework for agentic biomolecular discovery, specifically using single-stranded DNA aptamers as a "sandbox" to test LLM-based reasoning agents. The methodology involves a multi-agent pipeline (Analyst, Strategist, Designer, Scorer) that iterates through design-test-improve cycles. The agents utilize a suite of computational tools (ViennaRNA, Boltz-2, ESMFold2-NA, etc.) and statistical analysis (DESeq2, OLS regression) to interpret wet-lab sequencing data. A key methodological contribution is the rigorous statistical framework for correcting bead-background noise and distinguishing specific binding from non-specific matrix adhesion, which the agents learn to apply autonomously. The approach demonstrates that agents can diagnose failure modes (e.g., G-quadruplex cross-reactivity) and adjust design strategies accordingly, moving beyond simple generation to iterative refinement based on empirical feedback.
Results & evaluation
The experiments are rigorous and well-controlled. The authors test two targets: alpha-thrombin (a positive control with known aptamers) and myoglobin (a de novo target). They report statistically significant improvements in hit rates across three rounds (0% to 42.1% for thrombin, 10.2% to 33.5% for myoglobin). Single-clone titrations confirm that the top candidates identified by the pooled screen exhibit nanomolar affinity, validating the high-throughput assay. The paper also includes a critical negative result: benchmarking 12 existing computational predictors shows they perform near random chance, highlighting the necessity of the wet-lab loop. The inclusion of inter-operator reproducibility checks (r=0.84-0.96) strengthens the reliability of the data.
Reproducibility
The paper provides high reproducibility. It details the specific tools, versions, and licenses used. The statistical pipeline (DESeq2 + OLS regression) is clearly defined. The agent prompts and reasoning traces are partially shared in Table 1. However, the full agent code and specific prompt engineering details are not fully released, and the wet-lab protocol, while described, requires specialized equipment (MiSeq, magnetic beads, specific protein conjugates). The data availability statement promises release of full tables upon publication, which is standard but not yet fulfilled.
Limitations
The primary limitation is the lack of a direct head-to-head comparison against non-agentic baselines (e.g., standard SELEX or Bayesian optimization) under identical budget constraints. The authors acknowledge this, stating that prospective trials with control arms are needed. Additionally, the study is limited to aptamers; generalization to more complex modalities like proteins or antibodies is suggested but not demonstrated. The cost and time per round ($1-$3 per candidate, 1-2 weeks) are low, but still higher than purely in silico methods, limiting the scale of search possible.
Broader impact
This paper is significant for the AI for Science community as it provides a concrete, validated example of LLM agents closing the loop with physical experiments. It challenges the assumption that computational predictors alone can drive discovery in data-scarce domains. The "sandbox" concept of using aptamers to test agentic workflows before applying them to harder targets is a valuable strategic insight. It may influence the design of future self-driving laboratories and AI co-scientist frameworks. The paper demonstrates that LLM-based agentic pipelines can effectively iterate with wet-lab data to improve molecular design, achieving significant hit rates and nanomolar binders in a closed-loop aptamer discovery campaign. By rigorously benchmarking against computational predictors and showing their inadequacy, the study establishes the necessity of empirical feedback loops for AI-driven discovery in data-scarce biological domains, offering a robust framework for future self-driving laboratory systems.
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