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How this brief is selected: we include AI4Science updates with reliable sources and complete Chinese and English coverage, prioritizing fuller and easier-to-check information.
01Latent Space
Radical Numerics co-founder Eric Nguyen discusses genomic language models and biosecurity. He helped develop Evo and Evo 2; a separate Arc/Stanford team later used those models to generate and synthesize functional bacteriophage genomes. The interview distinguishes three sequence scales: an average human gene is about 60K, long genes can reach 2.3M, and the whole human genome is around 3B. Nguyen argues that models which increase biological capability can also help defense keep pace. The 2.3M figure describes a long gene, not the whole genome.
Discovery · XtalPi
**media[1229066]**Viva-Thera makes its public debut with four modalities pursuing HIV prevention, treatment and cure, supported by XtalPi's platform capabilities.Viva-Thera combines its first atomic-resolution MPER-TMD-CT structure with XtalPi's AI and automation platform to advance a small-molecule PCC.MPER vaccine immunogens elicit antibody levels approximately five times those of the study comparator in rhesus macaques; antisera retain antiviral activity at a 1:1,000 dilution.BOSTON and SHENZHEN, China, Sept. 23, 2026 /PRNewswire/ -- XtalPi (HKEX: 2228) announced that Viva-Thera, a biotechnology company it incubated and backs as a strategic shareholder, has nominated a preclinical candidate (PCC) targeting the membrane-proximal external region (MPER) of HIV-1 gp41. The partners completed discovery and optimization in seven months by combining Viva-Thera's atomic-resolution structural research with XtalPi's AI drug design and automation platform. In its public debut, Viva-Thera also reported functional vaccine results in rhesus macaques and progress in targeted gene delivery. The company is pursuing HIV prevention and cure through four modalities-vaccines, long-acting small molecules, gene therapy and cell therapy-with XtalPi providing computational design, predictive modeling and experimental automation across the portfolio.
Latent Space
How often do you get to talk to a guest who has both an Academy Award and who invented textbook machine learning algorithms? John Platt has an Oscar , two textbook algorithms , two named asteroids, and an Erdos-Bacon number of 6. This was easily the most fun bio of all the guests we’ve read to date. And the result was an epic and fun chat covering Google’s Empirical Research Assistance (ERA), how AI can help battle climate change, and tons of great stories about the co-evolution of science and AI. John’s colleague Dave Bacon likes to tease John that his career has been defined by being twenty years early to the next big thing. This may be convolutional neural networks (some credit him with coining the term), fusion research, quantum computing. John and Google have been working on solving some of humanity’s hardest problems with AI and computation for well over a decade now. Recently John and his team set their sights on using AI to solve any scientific problem that can be written down as a score. Google’s Empirical Research Assistance (ERA) John’s team has taken on many hard scientific problems over the years. In solving these, they noticed a pattern, many scientific problems can be reduced to what John calls a “scoreable task”. Once you have the score function, the goal is to find some code that maximizes the score. The hard part is in formulating the score, but once you have the score finding the maximizer can still be quite a lot of effort. John’s team set out to automate solutions to this general problem. This came out of the idea of an “auto-Kaggle” AI, which can solve any Kaggle problem you can throw at it. Kaggle is owned by Google, so all the data was ready and easily available to them! The result is Google’s Empirical Research Assistance or ERA ( paper , github , blog ). 1 ERA is surprisingly simple conceptually. Gemini (or your LLM of choice) keeps a running tree of past experiments (notebooks) and where they’re going. It’s a close cousin of Monte Carlo Tree Search : at each iteration the Upper Confidence Bound rule picks which notebooks are most promising to mutate. This is optimistic, not greedy, so sometimes even the fifth-best notebook gets chosen. Gemini then proposes mutations for each one, about ten at a time. The history of each branch is shared, so different leaves can learn from each other. “It’s almost like having a hyper-eager grad stu…
Nature Portfolio · Nature Machine Intelligence
Nature Machine Intelligence, Published online: 23 September 2026; doi:10.1038/s42256-026-01304-x Computational drug design often generates molecules that cannot be synthesized. Li and Lai show how large language models can predict precise structural edits to fix these molecules, outperforming traditional methods and preserving key features.
