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An AI-native experimental laboratory for autonomous biomolecular operations

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  • Burger, B. et al. A mobile robotic chemist. Nature 583, 237–241 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Boiko, D. A., MacKnight, R., Kline, B. & Gomes, G. Autonomous chemical research with large language models. Nature 624, 570–578 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Dai, T. et al. Autonomous mobile robots for exploratory synthetic chemistry. Nature 635, 890–897 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ha, T. et al. AI-driven robotic chemist for autonomous synthesis of organic molecules. Sci. Adv. 9, eadj0461 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, Z. et al. A multimodal robotic platform for multi-element electrocatalyst discovery. Nature 647, 390–396 (2025).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Jia, Y. et al. Robot-assisted mapping of chemical reaction hyperspaces and networks. Nature 645, 922–931 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Steiner, S. et al. Organic synthesis in a modular robotic system driven by a chemical programming language. Science 363, eaav2211 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Mehr, S. H. M., Craven, M., Leonov, A. I., Keenan, G. & Cronin, L. A universal system for digitization and automatic execution of the chemical synthesis literature. Science 370, 101–108 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Abolhasani, M. & Kumacheva, E. The rise of self-driving labs in chemical and materials sciences. Nat. Synth. 2, 483–492 (2023).

    Article 
    CAS 

    Google Scholar
     

  • Seifrid, M. et al. Autonomous chemical experiments: challenges and perspectives on establishing a self-driving lab. Acc. Chem. Res. 55, 2454–2466 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Darvish, K. et al. ORGANA: a robotic assistant for automated chemistry experimentation and characterization. Matter 8, 101897 (2025).

    Article 

    Google Scholar
     

  • Song, T. et al. A multiagent-driven robotic AI chemist enabling autonomous chemical research on demand. J. Am. Chem. Soc. 147, 12534–12545 (2025).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Touvron, H. et al. LLaMA: open and efficient foundation language models. Preprint at https://doi.org/10.48550/arXiv.2302.13971 (2023).

  • Introducing ChatGPT. OpenAI https://openai.com/index/chatgpt/ (2022).

  • Guo, D. et al. DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning. Nature 645, 633–638 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Introducing Claude 3.5 Sonnet. Anthropic https://www.anthropic.com/news/claude-3-5-sonnet (2024).

  • Brown, T. B. et al. Language models are few-shot learners. In Proc. Advances in Neural Information Processing Systems, Vol. 33 (eds Larochelle, H. et al.) 1877–1901 (Curran Associates, 2020).

  • Swanson, K., Wu, W., Bulaong, N. L., Pak, J. E. & Zou, J. The virtual lab of AI agents designs new SARS-CoV-2 nanobodies. Nature 646, 716–723 (2025).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Yu, B., Baker, F. N., Chen, Z., Ning, X. & Sun, H. LlaSMol: advancing large language models for chemistry with a large-scale, comprehensive, high-quality instruction tuning dataset. In Proc. First Conference on Language Modeling https://openreview.net/forum?id=lY6XTF9tPv (2024).

  • Jablonka, K. M., Schwaller, P., Ortega-Guerrero, A. & Smit, B. Leveraging large language models for predictive chemistry. Nat. Mach. Intell. 6, 161–169 (2024).

    Article 

    Google Scholar
     

  • Antunes, L. M., Butler, K. T. & Grau-Crespo, R. Crystal structure generation with autoregressive large language modeling. Nat. Commun. 15, 10570 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Frey, N. C. et al. Neural scaling of deep chemical models. Nat. Mach. Intell. 5, 1297–1305 (2023).

    Article 

    Google Scholar
     

  • Charting the Future of Biotechnology https://www.biotech.senate.gov/final-report/chapters/ (National Security Commission of Emerging Biotechnology, 2025).

  • Lu, X. et al. Enzymatic DNA synthesis by engineering terminal deoxynucleotidyl transferase. ACS Catal. 12, 2988–2997 (2022).

    Article 
    CAS 

    Google Scholar
     

  • Meiser, L. C. et al. Reading and writing digital data in DNA. Nat. Protoc. 15, 86–101 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Daher, R. K., Stewart, G., Boissinot, M. & Bergeron, M. G. Recombinase polymerase amplification for diagnostic applications. Clin. Chem. 62, 947–958 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wang, D. Prizes must recognize machine contributions to discovery. Nature 646, 261 (2025).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Gao, S. et al. Empowering biomedical discovery with AI agents. Cell 187, 6125–6151 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Ramos, M. C., Collison, C. J. & White, A. D. A review of large language models and autonomous agents in chemistry. Chem. Sci. 16, 2514–2572 (2025).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Learning to Reason with LLMs. OpenAI https://openai.com/index/learning-to-reason-with-llms/ (2024).

  • Ouyang, L. et al. Training language models to follow instructions with human feedback. In Proc. Advances in Neural Information Processing Systems, Vol. 35 (eds Koyejo, S. et al.) 27730–27744 (Curran Associates, 2022).

  • Wang, L., Zhang, X., Su, H. & Zhu, J. A comprehensive survey of continual learning: theory, method and application. IEEE Trans. Pattern Anal. Mach. Intell. 46, 5362–5383 (2024).

    Article 
    PubMed 

    Google Scholar
     

  • Tanaka, T. & Letsinger, R. L. Syringe method for stepwise chemical synthesis of oligonucleotides. Nucleic Acids Res. 10, 3249–3259 (1982).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hoose, A., Vellacott, R., Storch, M., Freemont, P. S. & Ryadnov, M. G. DNA synthesis technologies to close the gene writing gap. Nat. Rev. Chem. 7, 144–161 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lee, H. et al. Photon-directed multiplexed enzymatic DNA synthesis for molecular digital data storage. Nat. Commun. 11, 5246 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lewis, P. et al. Retrieval-augmented generation for knowledge-intensive NLP tasks. In Proc. Advances in Neural Information Processing Systems, Vol. 33 (eds Larochelle, H. et al.) 9459–9474 (Curran Associates, 2020).

  • Bayley, O., Savino, E., Slattery, A. & Noël, T. Autonomous chemistry: navigating self-driving labs in chemical and material sciences. Matter 7, 2382–2398 (2024).

    Article 
    CAS 

    Google Scholar
     

  • Bran, A. M. et al. Augmenting large language models with chemistry tools. Nat. Mach. Intell. 6, 525–535 (2024).

    Article 

    Google Scholar
     

  • Ruan, Y. et al. An automatic end-to-end chemical synthesis development platform powered by large language models. Nat. Commun. 15, 10160 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Balandat, M. et al. BoTorch: a framework for efficient Monte-Carlo Bayesian optimization. In Proc. Advances in Neural Information Processing Systems, Vol. 33 (eds Larochelle, H. et al.) 21524–21538 (Curran Associates, 2020).

  • Li, K. et al. Highly ordered DNA framework interface enables efficient enzymatic oligonucleotide synthesis. Adv. Sci. 12, e05868 (2025).

    Article 
    CAS 

    Google Scholar
     

  • Li, K. et al. DNA-DISK: automated end-to-end data storage via enzymatic single-nucleotide DNA synthesis and sequencing on digital microfluidics. Proc. Natl Acad. Sci. USA 121, e2410164121 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Takahashi, C. N., Nguyen, B. H., Strauss, K. & Ceze, L. Demonstration of end-to-end automation of DNA data storage. Sci. Rep. 9, 4998 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhirnov, V., Zadegan, R. M., Sandhu, G. S., Church, G. M. & Hughes, W. L. Nucleic acid memory. Nat. Mater. 15, 366–370 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Church, G. M., Gao, Y. & Kosuri, S. Next-generation digital information storage in DNA. Science 337, 1628 (2012).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Cherry, K. M. & Qian, L. Supervised learning in DNA neural networks. Nature 645, 639–647 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, C. et al. Parallel molecular data storage by printing epigenetic bits on DNA. Nature 634, 824–832 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yang, S. et al. DNA as a universal chemical substrate for computing and data storage. Nat. Rev. Chem. 8, 179–194 (2024).

    Article 
    PubMed 

    Google Scholar
     

  • Gottweis, J. et al. Accelerating scientific discovery with Co-Scientist. Nature 655, 487–496 (2026).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ghareeb, A. E. et al. A multi-agent system for automating scientific discovery. Nature 655, 497–505 (2026).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yao, S. et al. ReAct: synergizing reasoning and acting in language models. In Proc. 11th International Conference on Learning Representations https://openreview.net/forum?id=WE_vluYUL-X (2023).

  • Skarlinski, M. D. et al. Language agents achieve superhuman synthesis of scientific knowledge. Preprint at https://doi.org/10.48550/arXiv.2409.13740 (2024).

  • Wu, M. et al. AutoDNA code and data for autonomous biomolecular operations. Zenodo https://doi.org/10.5281/zenodo.20624108 (2026).

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