Building intelligence for the biological world
Our Approach
AI biology cannot scale and is approaching a physical bottleneck.
Models can now generate more plausible proteins than biology can test. They can propose vast libraries of proteins as enzymes, drugs, sensors, delivery systems, and molecular tools. But those ideas only become useful when they are built, measured, and linked back to the sequence data that produced them.
That last step is now the constraint.
The bottleneck is no longer only model capability; it is the experimental data layer. We need a mechanism for AI biology to interact with the physical world, measuring data at AI scale and learning from physical phenomena. This requires the ability to measure protein function at vastly greater rates and turn those measurements into reliable training data. It also requires that data to be continuously fed back into the model, allowing AI to autonomously navigate one of the most complex problems of our time.
AI biology is constrained by the experimental layer and requires a new platform. Without this change, biological engineering will proceed at only incremental speeds from where we are today.
DropCode is building the lab-in-the-loop architecture required for AI to interact with the biological world. This allows us to build the biological models required to solve protein engineering.
Whitepaper
We wrote a whitepaper explaining how we think about AI biology and how biology needs to be rethought for AI.
Careers
We are working on some of the hardest problems in techbio. If it has been your life mission to change the world of biology, AI or human life, come talk to us.
Contact us
We'll be in touch when there's something worth your time.
