Aptamers as a sandbox for lab-in-the-loop agentic molecular discovery
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 $α$-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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Prospecting the protein design landscape.Jakob R. Riccabona, Katharina T. Stonig, J. Meiler et al. · 2026 · FEBS Letters · Not summarized yet
Abstract
Generative AI has driven remarkable breakthroughs in protein design, enabling the rapid, computationally guided creation of high-affinity binders against diverse targets. While remarkable experimental success has been demonstrated, the confidence metrics used to filter and evaluate designs remain optimized for static protein interfaces and can fail when applied to underrepresented or conformationally complex targets. In this review, we outline the current landscape of deep learning-driven protein design pipelines, discuss tailored applications in peptide, small molecule, binder, vaccine, and antibody design, and argue that the integration of ensemble-based methods represents a promising aven…
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Beyond SELEX: AI-driven aptamers from sequence to structure to function.Bo-Ming Cui, X. Lian, Wei-Wei Song et al. · 2026 · Biotechnology Advances · Not summarized yet
Abstract
Conventional aptamer discovery by systematic evolution of ligands by exponential enrichment (SELEX) is often constrained by limited library capacity, PCR bias, and multi-round screening. The recent convergence of artificial intelligence with aptamer development is driving a paradigm shift from empirical screening toward rational, data-driven design. This Review presents a timely and comprehensive overview of AI integration across the entire aptamer development pipeline, with a particular emphasis on advances reported in 2024-2026. We begin by discussing bioinformatics databases and computational tools for high-throughput SELEX data analysis. We then review state-of-the-art strategies for sec…
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Agentic campaign control for high-throughput de novo binder designMinkyu Jeon, Jinyeop Song, Jina Kim et al. · 2026 · bioRxiv · Not summarized yet
Abstract
Progress in artificial intelligence has produced a rapidly growing ecosystem of methods for de novo protein design. With access to many specialized and often complementary tools, how does one use them effectively, especially with a finite compute budget? Here, we introduce Target-adaptive Rescue–Explore–eXploit (T-REX), an agentic protein binder design framework that orchestrates multiple state-of-the-art protein generative models and structure evaluators. Over the course of a design campaign, T-REX leverages large language model (LLM) agents to reason over accumulating outcomes and decide whether to rescue promising candidates, explore alternative settings, or exploit productive routes, whi…
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UnibindMD: An Integrated AI and Molecular Dynamics Platform for De Novo Peptide Binder DesignXuan Wu, A. Huang, Cong-Mei Fu et al. · 2026 · bioRxiv · Not summarized yet
Abstract
UnibindMD is an integrated computational platform that turns a protein target into validated de novo peptide-binder leads by chaining five orthogonal stages: deep-learning binder design (BindCraft), independent structure prediction and confidence assessment (AlphaFold3-class / Boltz-2), binding-affinity prediction (PRODIGY), developability profiling (ADMET-AI), and all-atom molecular-dynamics validation (GROMACS/CHARMM36m). The platform targets a persistent weakness of single-method design, namely that confidence metrics such as ipTM and pLDDT are self-consistent but not independent, so a design predicted to be confident may still be physically unstable. We demonstrate UnibindMD on three And…
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AnewDDE: An Agentic Drug Discovery Engine for Biomolecular Interaction Modelling and Closed-Loop DesignKai Liu · 2026 · bioRxiv · Not summarized yet
Abstract
Accurate modelling of biomolecular interactions is fundamental to drug discovery, yet current artificial intelligence (AI) workflows remain fragmented across structure prediction, affinity estimation, molecular design, and experimental decision-making. We introduce AnewDDE, an agentic Drug Discovery Engine that connects these capabilities into a closed-loop system for biomolecular interaction modelling and design. We demonstrate several components of this system: AnewFold delivers superior performance on challenging targets, including antibody–antigen complexes and molecular glues, providing more reliable structural hypotheses together with pocket identification and conformational analysis; …
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Abstract
De novo protein design campaigns remain bottlenecked by the inference compute time required to generate and rank candidates, limiting wider functional exploration. Here, we introduce and evaluate an epitope excision approach that truncates targets to local interface windows to accelerate both backbone diffusion and structure prediction. Across 15 targets and 3,666 designs, excised windows closely reproduced full-target binding interfaces (median C\alpha RMSD of 0.37 \AA) and made it practical to design binders against the 719-residue extracellular domain of fibroblast activation protein alpha (FAP) by cutting RFdiffusion and AlphaFold3 GPU time by ~ 88% in one of the epitopes. Pairing this w…
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Computationally Guided Design and Experimental Validation of ssDNA Aptamers Targeting the SARS-CoV-2 Spike ProteinJosé C. E. Márquez Montesinos, Ana Maria Bernardez, Yuliet Mazola et al. · 2026 · Biomolecules · Not summarized yet
Abstract
Aptamer development has traditionally relied on iterative, labor-intensive experimental approaches. Recent advances in bioinformatics and computational power now enable the rational in silico design of high-affinity nucleic acid ligands, drastically reducing time and cost. Here, we present an integrated computational pipeline for designing ssDNA aptamers targeting the receptor-binding domain (RBD) of the SARS-CoV-2 Spike (S) protein. A library of 4000 randomly generated 20-nucleotide sequences with defined base ratios (C:G:A:T = 3:3:2:2) was screened based on secondary structure and free energy. The 100 most stable sequences (ΔG ≤ −4.1 kcal/mol) were modeled in three dimensions, yielding 100…
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Validation and analysis of 12,000 AI-driven CAR-T designs in the Bits to Binders competition.Clayton W. Kosonocky, Alex M. Abel, Aaron L. Feller et al. · 2026 · Molecular Systems Biology · Not summarized yet
Abstract
Artificial intelligence (AI) methods for proteins have advanced rapidly, improving structure prediction and design, particularly for de novo binders. However, most evaluations emphasize binding affinity rather than higher-order biological function. We present Bits to Binders, a global competition evaluating de novo binder design in the context of chimeric antigen receptor (CAR) T cells. Teams from 42 countries submitted 12,000 designs of 80-amino acid de novo minibinders targeting human CD20 as CAR binding domains. Designs were screened by pooled CAR-T proliferation, identifying 707 designs exhibiting significant CD20-specific enrichment, with team hit rates from 0.6% to 38.4%. Top-performin…
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