Our Vision
Revolutionize pre-clinical drug development with deep learning technology integrating physics to predict protein dynamics and produce better drug candidates.
We predict protein dynamics
- Practically all current methods assume protein targets are static entities. Integrating protein dynamics into the process is the key to making drugs faster, better, and cheaper.
- With 20 years of experience and 30,000 citations, we are now combining protein dynamics with cutting-edge AI technology.
We use physics-guided AI
- Conventional machine learning methods require a large training dataset, which is unavailable for protein dynamics.
- We use physics-based deep learning methods to train our model for the specific protein target.
We bring time and cost savings to drug discovery
- Our method shortens the time for predicting physiological dynamics from 4 months to 4 hours.
- The rapid progress in the early stages of drug discovery allows for quicker securing of drug patents and gives our partners a competitive edge in the marketplace.
Meet Our Team
Our team is composed of PhDs with a diverse set of backgrounds, ranging from the hard sciences to business. By combining our expertise, we work together to innovate and push the boundaries of deep learning for drug discovery.
Dr. Katie Kuo
Co-Founder & CEO
PhD in Chemistry | Georgia Tech
Dr. Andrew Pang
Co-Founder & CTO
PhD in Physics | Georgia Tech
Dr. Shiyao Bao
Co-Founder & COO
PhD in Management | IESE Business School
Prof. JC Gumbart
Scientific Advisor
PhD in Physics | UIUC
Dunn Family Professor of Physics | Georgia Tech
