Charlotte Deane, professor of structural bioinformatics at the University of Oxford and upcoming speaker at the 14th Annual PEGS Europe Conference in Barcelona, joins moderator Brandon DeKosky, assistant professor of chemical engineering at the Massachusetts Institute of Technology, to discuss the use of machine learning in antibody structure prediction. 

In this episode, Deane talks about her lab's AI tools for high-throughput prediction pipelines and why collecting general antibody property data will produce better models. She also speaks about the importance of using and building publicly available data sets and her thoughts on what it will take to finally generate a complete antibody design from a computer. 

Links from this episode:  
University of Oxford Department of Statistics
SAbDAb: The Structural Antibody Database
PEGS Europe 
The Critical Assessment of protein Structure Prediction (CASP)