Google Research grounds AI agents in biological reality with building blocks for autonomous wet labs

Google Research outlined three projects aimed at closing the loop between in-silico AI design and real wet-lab experiments. The VERID framework builds a semantic digital twin of a lab workcell on top of the open-source PyLabRobot, giving agents live 3D state tracking, fast virtual dry runs, hardware safety checks, and synchronized video and action logs; in case studies an agent recovered mid-run from a plate-reader failure and improved accuracy more than fivefold in a closed-loop dye-mixture task. In an aptamer sandbox over three design-test-iteration cycles, agent-driven design raised confirmed on-target hit rates to 33.5% for myoglobin and 42.1% for α-thrombin, and in de novo binder design against FAP, excising the local binding pocket during folding sped each cycle more than eightfold and human-agent collaboration nearly doubled the in-silico hit rate of a scientist working alone.