eliseo papa

We simultaneously generated a single cell dataset from top gene knockouts, knockdowns, overexpressions, synthetic genes, and CAR signaling domains. The full set of potential phenotypes was only accessible by testing across perturbation classes, and the most divergent transcriptomic phenotypes correlated with the greatest CAR T cell functional enhancements.

**A grand unified theory on what will happen in biotech in the next 10-20 years** the two major forces reshaping industrial biotech in the next decade are: 1. China 2. AI - and they're critically linked how? China's low R&D cost basis democratizes execution by providing infrastructure to more drug developers (similar to how AWS helped cloud apps explode in 2010s) AI makes scientific information much more freely available; agents & lab automation increase R&D productivity as well as throughput, further deflating development costs What happens when many more translational ideas can be tried much more cheaply? Value starts accruing in the best ideas to try ie the value shifts earlier in the value chain if the cost of everything from preclinical R&D to clinical trials are dropping significantly due to combo of AI and China, the disparity between clinical stage vs early pipeline assets shrinks dramatically from the current order of magnitude difference The premium on true creativity, novel scientific insight, fundamentally new biology will 100x In a few years the top-of-industry drug hunters / translational biologists will command a hefty premium (maybe not $100m a year like current top AI scientists but ... maybe??) Even more provocatively, foundational models in translational biology that surface / accelerate novel biological hypotheses will suddenly capture outsized value When will a translational foundational model be worth more than a top 10 pharma co? sounds crazy... but like everything else --> slowly, then all at once

Introducing Finch, a new agent that fully automates data-driven discovery in biology. We are launching a closed beta for it today (sign up below). This is still early, but impressive, maybe similar to a good 1st yr grad student. In the video, see how it independently reproduces key findings from the Golub Lab's 2020 MetMap paper, including the fact that ADAM28 deletions are associated with breast cancer metastases to brain (fig 4b of the original paper). It also identifies several novel findings not already in the paper, like associations with EFNA5 and PTCH1 amplifications. Importantly, the prompt here is fully open-ended! We just ask the agent to explore the data. Similar to a first year grad student, it makes a bunch of silly mistakes, but also actually ends up finding some really cool stuff. And it works really fast by comparison... 1/3