Sampling CDR-H3 Ensembles with Antibody Language Models and Diffusion

Antibodies derive their binding specificity primarily from the third complementarity-determining region of the heavy chain (CDR-H3), a hypervariable loop characterized by extreme sequence diversity and conformational flexibility. Despite its functional importance, accurate structural prediction of CDR-H3 remains an open problem. Here we present H3Fold, a fast generative framework for CDR-H3 structure prediction that couples antibody-specific language models with a denoising diffusion model. Sequence context is provided by two pre-trained encoders: H3BERTa, trained on over 18M CDR-H3 sequences, and AbRoBERTa, trained on over 57M full-length variable regions. To address the scarcity of high-resolution antibody structures, we train the diffusion trunk under a progressive curriculum that transitions from general protein backbone loops to CDR-H3 loops. Systematic ablations isolate the contribution of each architectural choice to structural quality. On benchmark splits filtered for strict sequence identity to the training set, H3Fold generates geometrically plausible, low-RMSD conformations at a fraction of the inference cost of existing predictors. Since the diffusion trunk samples structural ensembles, it can integrate directly into iterative antibody design pipelines where speed and conformational diversity are decisive.