  The design and optimization of antibodies requires an intricate balance
across multiple properties. Protein inverse folding models, capable of
generating diverse sequences folding into the same structure, are promising
tools for maintaining structural integrity during antibody design. Here, we
present AntiFold, an antibody-specific inverse folding model, fine-tuned from
ESM-IF1 on solved and predicted antibody structures. AntiFold outperforms
existing inverse folding tools on sequence recovery across
complementarity-determining regions, with designed sequences showing high
structural similarity to their solved counterpart. It additionally achieves
stronger correlations when predicting antibody-antigen binding affinity in a
zero-shot manner, while performance is augmented further when including antigen
information. AntiFold assigns low probabilities to mutations that disrupt
antigen binding, synergizing with protein language model residue probabilities,
and demonstrates promise for guiding antibody optimization while retaining
structure-related properties. AntiFold is freely available under the BSD
3-Clause as a web server at https://opig.stats.ox.ac.uk/webapps/antifold/ and
and pip installable package at https://github.com/oxpig/AntiFold
