Medra Launches Reasoning Layer for Drug Discovery Robotics

As AI infrastructure for drug discovery continues to proliferate with reasoning workflows capable of generating hypotheses, candidate molecules, and experimental plans, Medra CEO Michelle Lee, PhD, argues that physical AI is the solution to addressing the next bottleneck: experimental validation at scale. 

“Building foundation models in biology that can predict and cure disease will take thousands of years of data generation,” Lee explained in an interview with GEN Edge. “The more I looked at the field, the more I realized that this data problem is actually a robotics problem.”   

In a new collaboration with the Defense Advanced Research Projects Agency (DARPA), Medra has launched AI Experimentalist, the scientific reasoning layer of its robotics platform. The system translates high-level research goals expressed in natural language into executable workflows that span the entire experimental cycle, from literature review, wet-lab execution, data analysis, and protocol refinement. 

In a blog post, Medra presents an example where scientists prompt to “build an Epidermal Growth Factor Receptor (EGFR) blocking antibody assay cascade.” AI Experimentalist can propose small optimizations in execution, including testing linear DNA templates in parallel, optimizing expression conditions, and feeding results immediately into the next run, for compounding time savings from days to hours. 

Partners can access AI Experimentalist through physical AI labs deployed on site at customer facilities or operated remotely through Medra’s flagship science laboratory, Medra Lab 001 (ML001), which unveiled in April and touts running experiments 24/7. Medra describes the 38,000 square foot facility as the largest autonomous lab in the United States. 

Artisanal nature 

In contrast to industrial automation, which has been powerful for repeatable tasks, such as combinatorial chemistry and screening, physical AI equips the same hardware with sensors to enable intelligent decision-making. 

While many robotics players in biology are focused on the manufacturing step, Medra has the ambitious goal of accelerating end-to-end drug discovery campaigns. 

“The artisanal nature of science is actually what makes certain experiments work and others fail,” said Lee. She noted that seemingly subtle variables, such as the angle of a pipette or the precise timing of mixing reagents, can have an outsized impact on experimental outcomes.  

Medra is currently working with partners across academia, biopharma, and government to run and develop assays across a wide array of applications, including antibody discovery, protein engineering, gene editing, and cell biology. 

Looking ahead, Lee says the bottleneck is not robotic capability, but integration and deployment. AI Experimentalist addresses this challenge through a multi-agent architecture and model-agnostic harness that allows Medra to incorporate new biological AI models and scientific agents. Among them are NVIDIA Nemotron models for protocol editing and optimization and the newly launched NVIDIA BioNeMo Agent Toolkit. 

“The flexibility of physical AI will be incredibly key in making scientific discovery truly autonomous,” asserts Lee.