(21g) 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. However, maximizing antibody properties such as affinity is often detrimental to other properties such as stability and specificity, which can compromise safety and efficacy. Due to inherent tradeoffs between drug-like biophysical properties, co-optimization of multiple antibody properties remains a difficult and time-consuming process that impedes drug development. This presentation will highlight our recent advances in developing machine learning methods to co-optimize several different antibody properties, including affinity, stability, self-association and non-specific binding. We will also discuss how these methods can be used to greatly expand the exploration of novel antibody sequence space and accelerate the development of highly potent, drug-like antibodies.