AI-Native Autonomous Biomolecule Research Laboratory

The source is a bibliography of 53 works tracing the development of autonomous scientific research. It covers mobile and robotic chemists, self-driving laboratories, large language models and AI agents, automated DNA synthesis and data storage, molecular computing, and multi-agent systems for scientific discovery, with publications dated from 1982 through 2026.

Autonomous chemistry and robotic laboratories

Berger, B. et al. “Mobile robot chemist.” Nature 583, 237–241 (2020). Article · PubMed · Google Scholar
Steiner, S. et al. “Organic synthesis in modular robotic systems driven by chemical programming languages.” Science 363, eaav2211 (2019). Article · PubMed · Google Scholar
Mehr, SHM, Craven, M., Leonov, AI, Keenan, G. & Cronin, L. “A versatile system for digitization and automated execution of chemical synthesis literature.” Science 370, 101–108 (2020). Article · PubMed · 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. “An AI-powered robotic chemist that autonomously synthesizes organic molecules.” Science Advances 9, eadj0461 (2023). Article · PubMed · PubMed Central · Google Scholar
Zhang, Z. et al. “A multimodal robotic platform for multi-element electrocatalyst discovery.” Nature 647, 390–396 (2025). Article · PubMed · Google Scholar
Jia, Y. et al. “Robot-assisted mapping of hyperspace and networks of chemical reactions.” Nature 645, 922–931 (2025). Article · PubMed · PubMed Central · Google Scholar
Abolhasani, M. & Kumacheva, E. “The rise of self-driving laboratories in chemistry and materials science.” Nature Synthesis 2, 483–492 (2023). Article · Google Scholar
Seifrid, M. et al. “Autonomous chemical experiments: Challenges and prospects for establishing self-driving laboratories.” Accounts of Chemical Research 55, 2454–2466 (2022). Article · PubMed · PubMed Central · Google Scholar
Darvish, K. et al. “ORGANA: A robotic assistant for automated chemical experiments and characterization.” Matter 8, 101897 (2025). Article · Google Scholar
Bayley, O., Savino, E., Slattery, A. & Noël, T. “Autonomous chemistry: Navigating self-driving labs in chemistry and materials science.” Matter 7, 2382–2398 (2024). Article · Google Scholar

Language models and AI agents in science

Boiko, D.A., MacKnight, R., Kline, B. & Gomes, G. “Autonomous chemical research using large-scale language models.” Nature 624, 570–578 (2023). Article · PubMed · PubMed Central · Google Scholar
Brun, AM et al. “Extending large-scale language models using chemical tools.” Nature Machine Intelligence 6, 525–535 (2024). Article · Google Scholar
Luan, Y. et al. “An automated end-to-end chemical synthesis development platform leveraging large-scale language models.” Nature Communications 15, 10160 (2024). Article · PubMed · PubMed Central · Google Scholar
Song, T. et al. “Multi-agent-driven robotic AI chemist enables autonomous chemical research on demand.” Journal of the American Chemical Society 147, 12534–12545 (2025). Article · PubMed · Google Scholar
Gao, S. et al. “Powering biomedical discovery with AI agents.” Cell 187, 6125–6151 (2024). Article · PubMed · Google Scholar
Ramos, MC, Collison, CJ & White, AD. “A review of large-scale language models and autonomous agents in chemistry.” Chemical Science 16, 2514–2572 (2025). Article · PubMed · Google Scholar
Swanson, K., Wu, W., Bulaong, NL, Pak, JE & Zou, J. “A virtual lab of AI agents designs new SARS-CoV-2 nanobodies.” Nature 646, 716–723 (2025). Article · PubMed · Google Scholar
Gottweis, J. et al. “Accelerate scientific discovery with collaborators.” Nature 655, 487–496 (2026). Article · PubMed · PubMed Central · Google Scholar
Gharib, AE et al. “Multi-agent systems for automating scientific discovery.” Nature 655, 497–505 (2026). Article · PubMed · PubMed Central · Google Scholar
Yao, S. et al. “ReAct: Synergizing inference and action in language models.” In Proceedings of the 11th International Conference on Learning Representations (2023).
Skarlinski, MD et al. “Linguistic agents achieve superhuman synthesis of scientific knowledge.” Preprint in arXiv (2024). arXiv

Language-model foundations and chemistry applications

Touvron, H. et al. “LLaMA: An open and efficient basic language model.” Preprint in arXiv (2023). arXiv
“Introducing ChatGPT.” OpenAI (2022). OpenAI
Guo, D. et al. “DeepSeek-R1 facilitates inference in LLM through reinforcement learning.” Nature 645, 633–638 (2025). Article · PubMed · PubMed Central · Google Scholar
“Introduction to Claude 3.5 Sonnet.” Anthropic (2024). Anthropic
Brown, TB et al. “Language models are trained a few times.” In Advances in Neural Information Processing Systems, Vol. 33 (Larochelle, H. et al., eds.), 1877–1901 (Curran Associates, 2020).
“Learn how to reason with LLM.” OpenAI (2024). OpenAI
Ouyang, L. et al. “Train language models to follow instructions from human feedback.” In Advances in Neural Information Processing Systems, Vol. 35 (Koyejo, S. et al., eds.), 27730–27744 (Curran Associates, 2022).
Wang, L., Zhang, X., Su, H. & Zhu, J. “A comprehensive survey of continuous learning: Theory, methods, and applications.” IEEE Transactions on Pattern Analysis and Machine Intelligence 46, 5362–5383 (2024). Article · PubMed · Google Scholar
Jablonka, KM, Schwaller, P., Ortega-Guerrero, A. & Smit, B. “Leveraging large-scale language models for predictive chemistry.” Nature Machine Intelligence 6, 161–169 (2024). Article · Google Scholar
Antunes, LM, Butler, KT & Grau-Crespo, R. “Generation of crystal structures by autoregressive large-scale language modeling.” Nature Communications 15, 10570 (2024). Article · PubMed · PubMed Central · Google Scholar
North Carolina Frye et al. “Neural scaling of deep chemical models.” Nature Machine Intelligence 5, 1297–1305 (2023). Article · Google Scholar
Yu, B., Baker, F.N., Chen, Z., Ning, X. & Sun, H. “LlaSMol: Advances in large-scale language models for chemistry using large, comprehensive, high-quality instruction coordination datasets.” In 1st Conference on Procedural Language Modeling (2024).
Lewis, P. et al. “Search extension generation for knowledge-intensive NLP tasks.” In Advances in Neural Information Processing Systems, Vol. 33 (Larochelle, H. et al., eds.), 9459–9474 (Curran Associates, 2020).
Balandat, M. et al. “BoTorch: A framework for efficient Monte Carlo Bayesian optimization.” In Advances in Neural Information Processing Systems, Vol. 33 (Larochelle, H. et al., eds.), 21524–21538 (Curran Associates, 2020).

DNA synthesis, storage and molecular computing

Lu, X. et al. “Enzymatic DNA synthesis by engineering terminal deoxynucleotidyl transferases.” ACS Catalysis 12, 2988–2997 (2022). Article · Google Scholar
Miser, LC et al. “Reading and writing digital data in DNA.” Nature Protocols 15, 86–101 (2020). Article · PubMed · Google Scholar
Tanaka, T. & Letsinger, R.L. “A syringe method for stepwise chemical synthesis of oligonucleotides.” Nucleic Acids Research 10, 3249–3259 (1982). Article · PubMed · PubMed Central · Google Scholar
Hoose, A., Vellacott, R., Storch, M., Freemont, PS & Ryadnov, MG. “DNA synthesis techniques to fill the gene writing gap.” Nature Reviews Chemistry 7, 144–161 (2023). Article · PubMed · PubMed Central · Google Scholar
Lee, H. et al. “Photon-directed multienzyme DNA synthesis for molecular digital data storage.” Nature Communications 11, 5246 (2020). Article · PubMed · PubMed Central · Google Scholar
Lee, K. et al. “The highly ordered DNA framework interface enables efficient enzymatic oligonucleotide synthesis.” Advanced Science 12, e05868 (2025). Article · Google Scholar
Lee, K. et al. “DNA-DISK: Automated end-to-end data storage by enzymatic single-nucleotide DNA synthesis and sequencing on digital microfluidics.” Proceedings of the National Academy of Sciences of the United States of America 121, e2410164121 (2024). Article · 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.” Scientific Reports 9, 4998 (2019). Article · PubMed · PubMed Central · Google Scholar
Zhirnov, V., Zadegan, RM, Sandhu, GS, Church, GM & Hughes, WL. “Nucleic acid memory.” Nature Materials 15, 366–370 (2016). Article · PubMed · PubMed Central · Google Scholar
Church, GM, Gao, Y. & Kosuri, S. “Next generation digital information storage in DNA.” Science 337, 1628 (2012). Article · PubMed · Google Scholar
Cherry, KM & Qian, L. “Supervised learning in DNA neural networks.” Nature 645, 639–647 (2025). Article · 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 · PubMed · PubMed Central · Google Scholar
Yang, S. et al. “DNA as a universal chemical substrate for computing and data storage.” Nature Reviews Chemistry 8, 179–194 (2024). Article · PubMed · Google Scholar
National Security Council for Emerging Biotechnologies. Drawing the future of biotechnology (2025). Report
Daher, RK, Stewart, G., Boissinot, M. & Bergeron, MG. “Recombinase polymerase amplification for diagnostic applications.” Clinical Chemistry 62, 947–958 (2016). Article · PubMed · PubMed Central · Google Scholar

Perspectives and related research

Wang, D. “Awards should recognize machines’ contributions to discovery.” Nature 646, 261 (2025). Article · PubMed · Google Scholar
“A virtual lab of AI agents designs new SARS-CoV-2 nanobodies” is also discussed in the source bibliography under AI-enabled biomedical discovery.
Ramos, MC, Collison, CJ & White, AD. “A review of large-scale language models and autonomous agents in chemistry.” Chemical Science 16, 2514–2572 (2025). Article · PubMed
“Accelerate scientific discovery with collaborators.” Nature 655, 487–496 (2026). Article
“Multi-agent systems for automating scientific discovery.” Nature 655, 497–505 (2026). Article
Wu, M. et al. “AutoDNA code and data for autonomous biomolecular manipulation.” Zenodo (2026). Zenodo

Source: www.nature.com