Atinary Technologies, based in Lausanne and Silicon Valley, is pioneering autonomous scientific experimentation with AI-driven robotic research labs. In this episode, our season finale, the Derby Mill panel welcomes Hermann Tribukait, co-founder and CEO of Atinary, whose vision is to use the company’s technology to exponentially accelerate the discovery of breakthrough molecules.
Named after the Spanish verb “atinar” — to hit a target — Atinary’s product is SDLabs, short for “Self-Driving Labs Platform,” a code-free agentic AI solution that enables scientists to optimize their experimentation workflows without coding or machine-learning expertise. The platform has been deployed across pharma, chemicals, and materials R&D.
Earlier this year, in a significant step from the digital to the physical, Atinary opened its first Self-Driving Lab in Boston, where it runs experiments autonomously 24/7. Reported efficiencies and scaling are impressive. Atinary says it can generate in a week the data that would take a grad student an entire Ph.D. For one client, Atinary’s self-driving experimentation cut the use of an expensive catalyst by 30x, reducing cost by 97% and reaction time by 50%.
Is autonomous scientific discovery realistic? And where might the technology go at the limit? “One place where all this leads,” observes Sendhil, “Chemistry will get one step closer to a solved field—and when chemistry gets close to a solved field, boy does that look completely different.”
PARTICIPANTS
Hermann Tribukait, co-founder, CEO and chair, Atinary Technologies Inc.
Ajay Agrawal, co-founder and partner, Intrepid Growth Partners
Sendhil Mullainathan, senior advisor, Intrepid Growth Partners, MacArthur Genius grant recipient and professor, MIT
Niamh Gavin, senior advisor, Intrepid Growth Partners, Applied AI scientist and CEO, Emergent Platforms
LINKS
Atinary website
Loïc Roch, Atinary CTO and co-founder, explains the technology
Atinary launches its first self-driving lab
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DISCUSSION POINTS
00:00 Cold open
00:52 Explaining Atinary
03:40 Sample objectives
05:54 DMTA and learn
07:22 Efficiencies
09:29 Scaling challenge
12:39 Why run experiments?
15:12 A solved field
16:30 Chemistry and RL
17:47 GenAI lessons
18:44 Football fields
20:22 Chemistry as biology
21:29 Formulation challenge
23:15 Combinatorial search
26:52 Programmable biology
28:57 OFAT
30:44 Lightning round
33:12 Eroom’s law
DISCLAIMER
The content of this podcast is for informational and educational purposes only and should not be construed as marketing, solicitation, or an offer to buy or sell any securities or investments. The opinions expressed in this video are those of the participants and do not necessarily reflect the views of Intrepid Growth Partners or its affiliates. Any discussion of specific companies, technologies, or industries is for illustrative purposes and does not constitute investment advice. Viewers are encouraged to consult with their own financial, legal, and tax advisors before making any investment decisions.
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