Google's AI agents ran real lab robots: binder hit rate 0% to 42.1%
Why it matters
Google Research grounded AI agents in VERID, a live 3D digital twin of a liquid-handling robot workcell: every protocol is dry-run in simulation and invalid commands are blocked before they reach the robot. Over 3 closed-loop design-test rounds of DNA aptamers, the alpha-thrombin hit rate went 0% → 26.0% → 42.1% (98.5% specificity). On myoglobin, agents spotted "sticky" non-specific binders, revised their own design rules and lifted the on-target hit rate ninefold to 33.5%. For FAP, a protein overexpressed in solid tumors, top designs reached single-digit nanomolar affinity in a few experimental runs. Excising just the binding pocket made each fold 8.6x faster, and at equal compute a scientist + agent nearly doubled a scientist's in-silico hit rate (9.33% vs 5.06%). Caveat: these are building blocks, not a fully autonomous lab; humans authorize designs and supervise robot runs, and every sequence gets biosecurity screening.
Read the source: Google Research (opens in a new tab)
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