Case Study

Machine Learning and Protein Design

A major pharmaceutical company wanted to develop novel vaccine antigens for the next generation of influenza vaccines and advance them to clinical trials.


Our primary responsibilities included designing novel influenza antigen candidates using a combination of structural biology, machine learning, and protein folding simulation, as well as maintaining close partnerships with wet lab colleagues to evaluate the antigen candidates experimentally.


Together with the wet lab team, we developed an iterative antigen design and screening platform from scratch,. This allowed us to evaluate nearly 1000 antigen candidates and identify those with desirable stability and immunogenicity suitable for pre-clinical and clinical evaluation. A provisional patent application has been prepared for late-stage influenza antigen candidates.