Intuitive enzyme design with LLM agents | Nature Computational Science

A collaborative system of large language model agents brings protein engineering closer to natural-language interaction, lowering the barrier between human intent and biomolecular design. This is a preview of subscription content, access via your institution Access options Access through your institution Access Nature and 54 other Nature Portfolio journals Get Nature+, our best-value online-access subscription 27,99 € / 30 days cancel any time Learn more Subscribe to this journal Receive 12 digital issues and online access to articles 111,21 € per year only 9,27 € per issue Learn more Rent or buy this article Prices vary by article type from $1.95 to $39.95 Learn more Prices may be subject to local taxes which are calculated during checkout Fig. 1: Schematic of enzyme design with MutexaGPT. Subjects Biocatalysis Computational biophysics Molecular dynamics References Shao, Q. et al. Nat. Comput. Sci. https://doi.org/10.1038/s43588-026-01049-y (2026). Article Google Scholar Wiesinger, J., Marlow, P. & Vuskovic, V. Agents. Kaggle https://www.kaggle.com/whitepaper-agents (2025). Ma, Z. et al. J. Chem. Inf. Model. 65 , 6599–6612 (2025). Article Google Scholar Ferruz, N., Schmidt, S. & Höcker, B. Nat. Commun. 13 , 4348 (2022). Article Google Scholar Nijkamp, E., Ruffolo, J. A., Weinstein, E. N., Naik, N. & Madani, A. Cell Syst. 14 , 968–978.E3 (2023). Article Google Scholar Ghafarollahi, A. & Buehler, M. J. Digit. Discov. 3 , 1389–1409 (2024). Article Google Scholar Ponnapati, M. et al. ProteinCrow: a language model agent that can design proteins. In Generative AI for Biology Workshop, Int. Conf. Machine Learning https://icml.cc/virtual/2025/51218 (ICML, 2025). Durumeric, A. E. P. et al. J. Chem. Inf. Model. 66 , 5721–5735 (2026). Article Google Scholar Dolorfino, M. et al. Assessing the generalizability of machine learning and physics-based methods with DNA-encoded libraries. Preprint at bioRxiv https://doi.org/10.64898/2026.04.18.719394 (2026). Download references Author information Authors and Affiliations Department of Medicinal Chemistry, University of Michigan, Ann Arbor, MI, USA Terra Sztain Department of Biophysics, University of Michigan, Ann Arbor, MI, USA Terra Sztain Authors Terra Sztain View author publications Search author on: PubMed Google Scholar Corresponding author Correspondence to Terra Sztain . Ethics declarations Competing interests The author declares no competing interests. Rights and permissions Reprints and permissions About this article Cite this article Sztain, T. Intuitive enzyme design with LLM agents. Nat Comput Sci (2026). https://doi.org/10.1038/s43588-026-01052-3 Download citation Published : 22 September 2026 Version of record : 22 September 2026 DOI : https://doi.org/10.1038/s43588-026-01052-3 Share this article Anyone you share the following link with will be able to read this content: Get shareable link Sorry, a shareable link is not currently available for this article. Copy shareable link to clipboard Provided by the Springer Nature SharedIt content-sharing initiative