Insider Brief
- Medra raised $52 million in Series A funding led by Human Capital to advance its “Physical AI Scientist” platform, which integrates robotics, AI, and lab infrastructure for automated experimentation.
- The company’s system autonomously runs end-to-end experiments and pairs with a Scientific AI co-pilot that interprets results and iteratively updates protocols, creating a closed feedback loop between predictions, execution, and data generation.
- Medra targets long-standing bottlenecks in pharma R&D, aiming to replace fragmented, manual workflows and partial lab automation with a continuous learning engine that can scale experiment volume, speed iteration, and improve drug discovery success rates.
PRESS RELEASE —Medra, the company developing the first platform for Physical AI Scientists, today announced a $52 million Series A financing led by Human Capital, with participation from existing investors Lux Capital, Neo, and NFDG, alongside new investors Catalio Capital Management, Menlo Ventures, 776, Fusion Fund, and others.
Medra’s Physical AI autonomously runs experiments end-to-end, interfacing with standard laboratory tools and instruments and allowing scientists to adapt workflows through natural-language instructions. Its companion system, Medra’s Scientific AI, interprets results and co-pilots protocol improvements to enhance experimental outcomes and create a continuous learning engine.
“Pharma runs millions of experiments, but most of that data can’t be reused or fed back into AI. We’re closing that loop by tying predictions to outcomes in a continuous, self-improving cycle,” said Michelle Lee, Ph.D., CEO & Founder, Medra. “To accelerate drug development, we need to link predictions directly to automated execution and feed the results back into the model. This continuous loop enables drug discovery companies to run far more experiments, iterate faster, and advance therapies with a higher probability of success.”
Current AI lab alternatives tend to fall at one end of the spectrum, offering either traditional industrial automation without meaningful machine learning or AI-driven software without any robotic execution. Bringing a new medicine to market still takes 10-15 years and over $2B because discovery and preclinical work are slow, manual, and fragmented.
Pharma has tried to fix this with partial lab automation that remains brittle, inflexible, and dependent on scientist intervention, while separately building ML programs that still require manual, time-consuming experiments to generate data. None of these efforts operate in a closed feedback loop, leaving experimentation, data generation, and model improvement disconnected. Medra solves this by unifying robotics, AI, and data generation into a continuous system.
“Medra is creating an entirely new category in biopharma R&D, one where we believe science can continuously learn and scale to create groundbreaking therapeutics with a higher chance of clinical success,” said Armaan Ali, Co-founder, CEO & Managing Partner, Human Capital.
Patrick Hsu, Assistant Professor at UC Berkeley and co-founder of the Arc Institute, added: “AI models are generating predictions far faster than we can validate them experimentally. Integrating these tools with traditional lab automation is often too rigid to scale effectively. Medra’s Physical AI Scientist bridges this gap using autonomous, general-purpose robotics. The system learns from every experiment, creating the continuous feedback loop needed to scale data generation and drive breakthroughs in frontier science.”
During JPM Week (January 12–16, 2026), Medra will host private tours of its San Francisco facility. For more information, contact tours@medra.ai.
