Frank Teets
Head, Computational Biology AI Proteins
Frank holds a Ph.D. in Computational Biology from the University of North Carolina, where he developed a requirement-driven protein design algorithm used to create the first fully rationally designed miniprotein libraries. As Head of Computational Sciences at AI Proteins, he leads the development of AI-driven algorithms supporting protein design and optimization. His work spans generative models as well as predictive tools for protein expression, stability, immunogenicity, and developability and the infrastructure required to deploy them. His experience sits at the intersection of AI strategy and experimental design, with a focus on building scalable tools that accelerate therapeutic protein development.
Seminars
- Understand how immunogenicity, stickiness, and other liabilities frequently cannot be solved in isolation without worsening other properties
- Explore how In silico predictors can be tuned to not only guide efforts to verify predicted risks but also unify potential solutions in sequence space
- Discover a unified framework for evaluating and allocating effort to solving developmental liabilities, including toxicity, can significantly accelerate development efforts
