MOSAIC: Conditional Structural Memory for Antibody and Nanobody Design

Antibody and nanobody design requires jointly modeling local CDR sequence and structure and global antibody placement relative to a target antigen. Existing generators encode reusable structural patterns only implicitly and often struggle to coordinate these two objectives. Explicit retrieval offers reusable structural context, but similar antigens may support distinct antibody interfaces. We introduce MOSAIC, a conditional structural-memory method that augments a frozen flow-matching generator with diverse training examples selected using inference-time conditions. MOSAIC transfers retrieved sequence and layout features through separate, selectively gated pathways for CDR refinement and antibody pose correction. A null route allows the CDR adapter to abstain from memory use, while a calibrated support gate attenuates poorly supported pose corrections. Across VH–VL and VHH benchmarks, MOSAIC improves docking accuracy and success rates over its no-memory counterpart in both design and structure prediction, and achieves the best DockQ and success rates among the evaluated design methods. Full-test-set paired comparisons support the benefit of memory, while randomized-retrieval and component ablations on VH–VL support condition-dependent selection and complementary refinement pathways. These results demonstrate that explicit structural memory can improve antibody–antigen complex generation without updating the generative backbone.