Peter Tessier is the Albert M. Mattocks (Endowed) Professor in the Departments of Chemical Engineering, Pharmaceutical Sciences and Biomedical Engineering, and a member of the Biointerfaces Institute and Chemical Biology Program at the University of Michigan in Ann Arbor, MI. In this presentation, he discusses "Simplifying complex antibody engineering using machine learning".
Therapeutic antibody development requires selection and engineering of molecules with high affinity and other drug-like biophysical properties. Here we demonstrate the use of in vitro antibody library selections, high-throughput screening, and machine learning to greatly simplify the identification of antibodies with co-optimal levels of affinity and multiple key biophysical properties (non-specific binding, self-association, and stability).
