Review of Learning-Based Antibody Design: From Sequence to Structure

Improving antibodies' affinity and specificity has traditionally relied on iterative display selections or structure-based design, both costly and time-intensive. Recent advances in Deep Learning offer data-driven priors that effectively narrow the sequence space before expensive experiments. This paper provides an overview of the progress and challenges of learning -based antibody design. Adopting a pipeline-first perspective, this review organises current methods into three categories: (A) sequence-only protein language models (PLMs); (B) structure-aware strategies, including inverse folding and complex-aware optimisation; and (C) integrated AI-physics workflows. To avoid mixing endpoints, prospective wet-lab outcomes (e.g. hit rates, affinity gains) are reported separately from structure-linked surrogates (e.g. region recovery, refold root-mean-square deviation (RMSD), deep mutational scanning (DMS) correlation). Evidence indicates that sequence-only PLMs are effective for low-budget screening, inverse folding methods provide backbone-conditioned ranking and structure-preserving edits, and lightweight AI-physics overlays help prioritise manufacturable candidates. A concise method -selection guide is provided for different data availability scenarios.