  Accurate prediction of antibody structure is a central task in the design and
development of monoclonal antibodies, notably to understand both their
developability and their binding properties. In this article, we introduce
ABodyBuilder3, an improved and scalable antibody structure prediction model
based on ImmuneBuilder. We achieve a new state-of-the-art accuracy in the
modelling of CDR loops by leveraging language model embeddings, and show how
predicted structures can be further improved through careful relaxation
strategies. Finally, we incorporate a predicted Local Distance Difference Test
into the model output to allow for a more accurate estimation of uncertainties.
