Grounding AI agents in biological reality: Building blocks for autonomous wet labs
What happened
Google Research presents three projects connecting AI agents with wet-lab experimentation through VERID laboratory automation, closed-loop aptamer discovery, and de novo protein binder design. Across three rounds, aptamer on-target hit rates reached 33.5% for myoglobin and 42.1% for α-thrombin; the protein design pipeline accelerated computational cycles more than eightfold and produced binders with single-digit nanomolar affinity. Integrating these projects remains a next step, with safeguards including sequence screening, simulation before robot execution, and human authorization and supervision.
Why it matters
The aptamer experiments illustrate how experimental feedback can expose misleading optimization targets, offering a transferable approach to distinguishing genuine biological improvements from artifacts in automated discovery workflows.
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