Blake Lash

At Edison, our goal is to bring superintelligence to medicine. Today, we’re thrilled to announce an agreement with @pfizer to deploy our AI scientist, Kosmos, across Pfizer’s R&D pipeline. By learning from Pfizer’s robust repositories of data across oncology, immunology, internal medicine and more, Kosmos will enable every program, experiment and clinical readout to sharpen Pfizer’s R&D decision-making and accelerate their pipeline, with compounding advantages to the discovery and development of life-saving medicines.

AI has transformed how we design therapeutics. But targeted delivery is still an expensive guessing game. Today @BobbyHollings and I are launching @deliverome with @beckypferdehirt and @radialscience at @AsteraInstitute, to fix that. 🧵

Announcing our new preprint! We built SPICE, a framework that combines large-scale experiments and generative AI to design RNA sequences that control cell type-specific gene expression using alternative splicing - a powerful new modality! (1/10) Preprint: biorxiv.org/content/10.1101/…

Today, we’re announcing Kosmos, our newest AI Scientist, available to use now. Users estimate Kosmos does 6 months of work in a single day. One run can read 1,500 papers and write 42,000 lines of code. At least 79% of its findings are reproducible. Kosmos has made 7 discoveries so far, which we are releasing today, in areas ranging from neuroscience to material science and clinical genetics, in collaboration with our academic beta testers. Three of these discoveries reproduced unpublished findings; four are net new, validated contributions to the scientific literature. AI-accelerated science is here. Our core innovation in Kosmos is the use of a structured, continuously-updated world model. As described in our technical report, Kosmos’ world model allows it to process orders of magnitude more information than could fit into the context of even the longest-context language models, allowing it to synthesize more information and pursue coherent goals over longer time horizons than Robin or any of our other prior agents. In this respect, we believe Kosmos is the most compute-intensive language agent released so far in any field, and by far the most capable AI Scientist available today. The use of a persistent world model also enables single Kosmos trajectories to produce highly complex outputs that require multiple significant logical leaps. As with all of our systems, Kosmos is designed with transparency and verifiability in mind: every conclusion in a Kosmos report can be traced through our platform to the specific lines of code or the specific passages in the scientific literature that inspired it, ensuring that Kosmos’ findings are fully auditable at all times. We are also using this opportunity to announce the launch of Edison Scientific, a new commercial spinout of FutureHouse, which will be focused on commercializing our agents and applying them to automate scientific research in drug discovery and beyond. Edison will be taking over management of the FutureHouse platform, where you can access Kosmos alongside our Literature, Molecules, and Precedent agents (previously Crow, Phoenix, and Owl). Edison will continue to offer free tier usage for casual users and academics, while also offering higher rate limits and additional features for users who need them. You can read more about this spinout on our blog, below. A few important notes if you’re going to try Kosmos. Firstly, Kosmos is different from many other AI tools you might have played with, including our other agents. It is more similar to a Deep Research tool than it is to a chatbot: it takes some time to figure out how to prompt it effectively, and we have tried to include guidelines on this to help (see below). It costs $200/run right now (200 credits per run, and $1/credit), with some free tier usage for academics. This is heavily discounted; people who sign up for Founding Subscriptions now can lock in the $1/credit price indefinitely, but the price ultimately will probably be higher. Again, this is less chatbot and more research tool, something you run on high-value targets as needed. Some caveats are also warranted. Firstly, we find that 80% of Kosmos findings are reproducible, which also means 20% are not -- some things it says will be wrong. Also, Kosmos certainly does produce outputs that are the equivalent to several months of human labor, but it also often goes down rabbit holes or chases statistically significant yet scientifically irrelevant findings. We often run Kosmos multiple times on the same objective in order to sample the various research avenues it can take. There are still a bunch of rough edges on the UI and such, which we are working on. Finally, we are aware that the 6 month figure is much greater than estimates by other AI labs, like METR, about the length of tasks that AI Agents can currently perform. You can read discussion about this in our blog post. Huge congratulations to our team that put this together, led by @ludomitch and @michaelathinks: Angela Yiu, @benjamin0chang, @sidn137, Edwin Melville-Green, Albert Bou, @arvissulovari, Oz Wassie, @jonmlaurent. A particular shout out to @m_skarlinski and his team that rebuilt the platform for this launch, especially Andy Cai @notAndyCai, Richard Magness, Remo Storni, Tyler Nadolski @_tnadolski, Mayk Caldas @maykcaldas, Sam Cox @samcox822 and more. This work would not have been possible without significant contributions from academic collaborators @mathieubourdenx, @EricLandsness, @bdanubius, @physicistnevans, Tonio Buonassisi, @BGomes_1905, Shriya Reddy, @marthafoiani, and @RandallBateman3. We also want to thank our numerous supporters, especially @ericschmidt, who has been a tremendous ally. We will have more to say about our supporters soon!

The Wilkinson Lab is open for science! @MSKCancerCenter 🧬We'll be finding funky new RNA biology, mainly by looking at reverse transcriptases (i.e. the Best Enzymes In The World)🧬

Many of the most complex and useful functions in biology emerge at the scale of whole genomes. Today, we share our preprint “Generative design of novel bacteriophages with genome language models”, where we validate the first, functional AI-generated genomes 🧵

Can we use RNA to insert kilobases of DNA into the human genome, opening up a new way to engineer cells or treat disease? Excited to share our latest work turning a retrotransposon (a “jumping gene” with an RNA intermediate) from songbirds into a genome editing tool. 🧵 (1/7)

Excited to share DIRECTED (Delivery to Intended REcipient Cells Through Envelope Design): a programmable platform for cell-type-specific delivery. rb.gy/c3rdw Thank you to @FrangiehChris, @mircoscopy, @GuilhemFaure, Rhiannon Macrae, @zhangf 1/7

Delivery of therapeutic molecules is a major bottleneck for treating a wide range of diseases. Today we describe a new modality for delivering proteins based on an engineered contractile injection system @nature nature.com/articles/s41586-0…

New preprint 🎇! 🧵about @JunjieXu15's exciting work on another retrotransposon derived Gag-like gene that has ALSO retained the ability to make beautiful virus-like capsids! 5 years ago we showed that Arc forms endogenous capsids and an obvious Q /1 sciencedirect.com/science/ar…

Let us answer your ❓❓❓ about the PhD application process. This virtual event will include overview of the application process and student panel and is OPEN TO ALL interested. Good info for applying to any bioeng PhD program. Learn more and register ow.ly/mrC750LaFY5

Did you know that viruses can enter cells via the T or B cell receptor? Excited to share our first steps in the @MEBirnbaum lab toward a viral technique for antigen ID @MITdeptofBE @kochinstitute @ragoninstitute #LoveVirology #Immunology #Tcells #Bcells biorxiv.org/content/10.1101/…

CRISPR systems are quite diverse, including some, like CRISPR-Cas13, which target RNA. @soumya_kannan12 & @altaetran found tiny versions of Cas13b (Cas13bt) that can be used to create RNA editors small enough to fit into a single AAV for in vivo delivery. nature.com/articles/s41587-0…

We are really excited to share our work on a new modular RNA delivery technology called SEND, built from endogenous components found in the mammalian genome. @ScienceMagazine science.sciencemag.org/conte…

Horizontal Transfer of Histone H3 by Mammalian Cells biorxiv.org/cgi/content/shor… #bioRxiv

Beyond excited to share that the Jin lab is opening this summer @scrippsresearch San Diego! Grateful for the support from my awesome mentors & colleagues, only to pay it forward. Lab is actively recruiting - pls see website for the info. RT appreciated! jinlab.squarespace.com

Tan et al. engineered a matrix binding form of Interleukin-1 receptor antagonist (IL-1Ra) that has the ability of precise localization and retention in the tissue. nature.com/articles/s42003-0… @ARMILabs @EMBLAustralia @Martino_Lab