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AI accelerates drug discovery by predicting candidates and reducing costs, but success depends on high-quality data and integration with lab systems to close the data loop and validate predictions.
This paper presents an AI agent that integrates large language models with laboratory orchestration software, allowing scientists to create, monitor, and manage automated lab protocols using natural language. Evaluated on three simulated labs, the agent achieves a 97% first-attempt protocol generation success rate and requires far fewer interface actions.
The Institute of Science Tokyo has launched a fully automated medical research laboratory operated entirely by robots, including the Maholo LabDroid humanoid, with plans to scale to 2,000 units by 2040. This initiative aims to automate the full scientific discovery pipeline to address researcher shortages and minimize human error in experimental workflows.