Discovery Intelligence for drug hunting teams.
AiMiner turns discovery questions into structured reports with actionable recommendations for scientists to approve.
- Disease
Target- Prior knowledge
AiMiner
Example report
Design proposal
- Scientific evidence
- For
- Against
- Next experiments
- Go/no-go recommendation
- Go
- Scientist approval
Each research round builds on prior learning.
Long-term memory
- Program goals
- Tested hypotheses
- Team decisions
- Experimental outcomes
UC Berkeley
The team behind AiMiner
- Molecular design with language models and cofolding
- Led computational discovery for an NIH-funded antiviral program
- Physics-based modeling and simulation
- Force fields and free energy calculations
- Lead designer of AiMiner
- University Endowed Chair in Computational Science, UC Berkeley
- 30+ years in computational chemistry
- Fellow of the American Chemical Society
