Google explores biologically grounded AI for autonomous wet labs

AI models are very good at predicting how molecules should behave. Biology is famously bad at taking direction. Google Research is trying to close that gap. In a blog post dated October 16, 2026, the team laid out building blocks for autonomous wet labs, where AI agents design experiments, run them on physical robotics, read the results, and try again. The goal is AI that learns from what actually happens in a test tube, not just from what a simulation says should happen. Meet VERID, the lab’s digital stunt double The centerpiece is a framework called VERID, short for VERified Infrastructure for Discovery Laboratories. Google describes it as a semantic digital twin of the lab. According to Google, VERID supports three core functions. Agents can query the system to inspect its state, run time-compressed simulations, and execute protocols safely on physical hardware. With those guardrails in place, the agents can run closed-loop cycles. They design a batch of candidates, test them, analyze the outcomes, and refine the next batch. From zero hits to 42.1% The headline result involves aptamers, short strands of DNA that fold into shapes capable of gripping a target molecule. Google’s ag...