Learning protein function through autonomous experimental interaction
Biological AI learns primarily from existing observations, but many questions cannot be answered from available data alone. Here we show that AI can instead acquire knowledge by acting directly on biological systems and learning from the consequences. We developed a closed-loop framework in which autonomous agents design protein variants, construct and characterize them in a robotic laboratory,…
We haven't written up this one. bioRxiv has the full story — the link below goes straight to it.
