  Antibodies, a prominent class of approved biologics, play a crucial role in
detecting foreign antigens. The effectiveness of antigen neutralisation and
elimination hinges upon the strength, sensitivity, and specificity of the
paratope-epitope interaction, which demands resource-intensive experimental
techniques for characterisation. In recent years, artificial intelligence and
machine learning methods have made significant strides, revolutionising the
prediction of protein structures and their complexes. The past decade has also
witnessed the evolution of computational approaches aiming to support
immunotherapy design. This review focuses on the progress of machine
learning-based tools and their frameworks in the domain of B-cell immunotherapy
design, encompassing linear and conformational epitope prediction, paratope
prediction, and antibody design. We mapped the most commonly used data sources,
evaluation metrics, and method availability and thoroughly assessed their
significance and limitations, discussing the main challenges ahead.
