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01Discovery · Anthropic
This September 22 secondary report describes Anthropic’s Bay Area biology laboratory and reports plans for Claude-directed robotic experiments. In its September 23 announcement, Anthropic explicitly states that all lab work in the laboratory it describes is performed by human scientists. Claude searches genomic data, proposes candidate systems and assists analysis. The two sources differ in date and claim; the earlier report does not establish an operating autonomous robotic laboratory. The function of the ART enzyme system described in the official announcement remains under investigation.
Discovery · Insilico Medicine
Rapid Oral presentation will report initial Phase I data for ISM6331 in patients with advanced solid tumors FDA Fast Track- and Orphan Drug-designated program reflects Insilico’s strategy to advance AI-designed medicines against difficult-to-treat, age-associated diseases CAMBRIDGE, Mass. — September 22, 2026 — Insilico Medicine ("Insilico"; HKEX: 3696), a clinical-stage generative artificial intelligence (AI)-driven drug discovery company, today reminded the oncology community that initial Phase I data for ISM6331, its investigational pan-TEAD inhibitor, will be presented in a Rapid Oral session at the European Society for Medical Oncology (ESMO) Congress 2026, taking place October 23–27 in Madrid, Spain. The presentation will provide the first reported clinical findings from the global, multicenter first-in-human study of ISM6331 in patients with advanced solid tumors. ISM6331 is designed to inhibit all four TEAD family transcription factors and disrupt tumor-promoting signals driven by dysregulation of the Hippo pathway, a central regulator of cell growth, survival, tissue homeostasis and regeneration. Presentation details Title: First-in-human Multicenter Phase 1 Study of ISM6331, an AI-Designed Pan-TEAD Inhibitor, in Patients with Mesothelioma or Other Advanced Solid Tumors Abstract number: #997 Session: Rapid Oral Presentation: Developmental Therapeutic Format: Rapid Oral presentation Presenter: Melina E. Marmarelis, MD, Hematology and Oncology, Abramson Cancer Center, University of Pennsylvania Date: Sunday, October 25, 2026 Time: 8:30–10:00 a.m. CEST Location: Cordoba Auditorium – Hall 4 “The ESMO presentation is an important opportunity to share the first clinical findings from ISM6331 with the global oncology community,” said Halle Zhang, PhD, Vice President, Clinical Development – Oncology at Insilico Medicine. “Patients with advanced mesothelioma and other Hippo pathway-dysregulated tumors have a significant unmet medical need. We look forward to presenting the emerging safety, pharmacokinetic and antitumor findings from this first-in-human study and discussing the next stage of the program.” From generative chemistry to clinical validation Insilico nominated ISM6331 as a preclinical candidate in June 2023. Its novel, non-covalent scaffold was generated with support from Chemistry42, the company’s generative chemistry platform, using structure-b…
Nature Portfolio · 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 Nature and 54 other Nature Portfolio journals Get Nature+, our best-value online-access subscription 27,99 € / 30 days cancel any time Subscribe to this journal Receive 12 digital issues and online access to articles 111,21 € per year only 9,27 € per issue Rent or buy this article Prices vary by article type from$1.95 to$39.95 Prices may be subject to local taxes which are calculated during checkout References - Shao, Q. et al. Nat. Comput. Sci. https://doi.org/10.1038/s43588-026-01049-y (2026). - 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). - Ferruz, N., Schmidt, S. & Höcker, B. Nat. Commun. 13, 4348 (2022). - Nijkamp, E., Ruffolo, J. A., Weinstein, E. N., Naik, N. & Madani, A. Cell Syst. 14, 968–978.E3 (2023). - Ghafarollahi, A. & Buehler, M. J. Digit. Discov. 3, 1389–1409 (2024). - 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). - 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). Ethics declarations Competing interests The author declares no competing interests. Rights 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 - Published: - Version of record: - DOI: https://doi.org/10.1038/s43588-026-01052-3
Nature Portfolio · Nature Computational Science
Nature Computational Science, Published online: 22 September 2026; doi:10.1038/s43588-026-01051-4 Embodied intelligence has enabled machines to act autonomously, yet intelligence remains largely confined to digital models, leaving robotic bodies as passive executors. In extreme environments, such as the human brain, where tissues are mechanically and thermally sensitive, conventional electronics can become limiting. Here, we discuss how active materials, which embed intelligence directly within their structure, can expand robotic capabilities in these settings, with a particular focus on the role of computation-aided design.
Discovery · OpenAI
Two weeks ago OpenAI claimed a solution to one of the biggest open problems in math—the Navier-Stokes problem—an achievement worth a $1-million prize from the Clay Mathematics Institute. The proof ignited a powder keg of concern over artificial intelligence companies’ race to disrupt the subject. But with the dust still far from settled, a different controversy is emerging: Did OpenAI even solve the right Navier-Stokes problem? Generated by an internal large language model (LLM), OpenAI’s proof relies on an approach that many experts find unnatural. It solves a variant of the problem that mathematicians say is disconnected from reality and thus less interesting. In a sense, the LLM found and exploited a loophole in the framing of the question. On supporting science journalism If you're enjoying this article, consider supporting our award-winning journalism by subscribing. By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today. “The most important problem is unsolved,” says Luis Silvestre, a mathematician at the University of Chicago. “The Clay problem is settled, but the main problem for the Navier-Stokes equations is not.” Furthermore, last Thursday, three mathematicians posted a proof of their own that showed that OpenAI’s method can never be extended to solve the full problem. In other words, the loophole will never be closed, barring some completely new idea. The Navier-Stokes equations are supposed to describe how fluids flow, but mathematicians doubt whether they can always be trusted. The million-dollar problem is about whether the equations ever “blow up,” which would mean they’d allow the flow to be infinitely fast at points—something that can’t happen in the real world. But there’s one piece of the equations that’s optional—sometimes it’s there; sometimes it isn’t. The “it” here is an external force such as gravity that affects how a fluid moves. “All fluids we know of are under some kind of external force,” says mathematician Diego Córdoba. “So to have the force makes complete sense.” When most experts think about the Navier-Stokes problem, though, it’s without this force. They want to find a purer, more fundamental way for the equations to blow up by using only the intrinsic forces within any fluid—not by applying some specific, precisely contrived external f…
