Foundational models are evolving, and next-gen AI could unlock new ways of doing scientific discovery.
At AIxBio, @pushmeet and @salcandido discussed data and scaling, dynamics, function and design, interpretability, and the path to the clinic, moderated by Brandon Anderson of
During the Unlocking the Future of Biology panel at our AIxBio event, Brad Bower of @NIH, Biohub's @ShanaOKelley, and Dorothy Koch of @ENERGY brought up AI-ready data and why it takes an international effort.
Data generation is essential to building predictive models of life
🧵 Biohub, @ENERGY, @NIH, and new funding partners today announced a major expansion of an international effort to generate and make accessible the data enabling predictive AI models of biology. Together, the organizations are investing $1.8 billion in funding, data, computation
Biohub Investigator @james_y_zou built a “virtual biotech”—37K AI agents that analyzed 57K clinical trials and found clues to what makes drugs succeed. @nytimes traces its roots to a collaboration sparked by our scientist John Pak: bit.ly/4hLrpnm
Three webinars on the science behind ESMC interpretability, ESMFold2, and binder design are now on YouTube. Hear from the researchers who built the models, and view live demos and tutorials that you can apply to your own research. Watch the playlist: