Phil Leung

My doctoral advisor @UWMolES / @UWproteindesign (and current scientific advisory board membe @Xaira_Thera) David Baker won the nobel prize!! So stoked and extremely determined to deliver on the promise of this tech! nobelprize.org/prizes/chemist…

anthropic.com/research/inves…
Vaults 👌

At @Xaira_Thera we are closing the loop.

SOTA models for molecular design and biological understanding (X-Design and X-Cell) allow us to design functional molecules for undruggable targets at unprecedented scale, and to increase our ability to decipher complex disease biology.

At the same time, massive data generation efforts in the "X-Lab" fill in the gaps in public data and allow us to ask questions that haven't been asked before, which in turn makes the models themselves, and the harness in which we run them, even better.

Today, I wanted to share my thoughts on two tool layers we're building to accelerate these efforts.

The first tool is a software layer. X-Scientist is a self improving agent harness compatible with many frontier LLMs (including recent Claude models from @AnthropicAI and GPT Astra from @OpenAI). This harness achieves SOTA performance on a number of tasks relevant to our design and data generation efforts, and allows humans to extend their reach to larger experiments and accelerate artisanal data analysis and design pipelines, including large-scale protein design campaigns.

The second tool is a hardware layer that integrates natively with our software stack. X-Automate is a robotics ecosystem that powers the X-Lab. X-Automate combines X-Bots (robots which work alongside human scientists in the wetlab), and X-Scientist, to coordinate workflows and monitor activities. We demonstrate X-Automate in a use case where it helps to bridge the gap between walk-up automation and analytical biochemistry, empowering scientist to hand off routine activities like sample loading.

Both of these two tool layers are truly in their infancy and I expect they may be able to contribute substantially to our goal of bringing 10x more medicine to 10x more patients in the next 10 years.

xaira.com/news/x-automat…

xaira.com/news/x-scienti…

This paper has excellent experiments and pretty mixed α/β folds (6 crystal structures..!) Also proteases are very useful research tools. Great stuff. Congrats @kinasekid and coauthors!

AI native biotech companies with wet labs dedicated to data generation have a straightforward path to becoming some of the most interesting RL sandboxes.
Check out this cool whitepaper from @leashbio @Andrewdblevins @allmeasures and colleagues.

Idea goes basically like this:
1. Synthesize a combinatorial library that enumerates a chemical space.
2. Pre-measure binding enrichment for all the combinations for a large number of protein targets.
3. Give Claudes the chemical components of the library and let them try to put them together to get binders with a limited budget and learn from each attempt.

github.com/Leash-Labs/min…