MoDA: a Modality-Adaptive Flow Matching Framework for Antibody Co-Design

Antibody co-design methods usually model modalities uniformly, overlooking biophysical heterogeneity in scale-dependent constraints, residue roles, and modality-specific contextual requirements. To address these challenges, we propose MoDA, a modality-adaptive flow matching framework that integrates multi-scale constraints with residue role modeling. We first develop multi-scale constraint modeling to capture disentangled semantic information from local regions to global complex levels via decomposing functional unit representations. Then, we introduce cooperation-aware role modeling to infer residue roles based on residue-specific constraints and inter-residue coordination. Coupling the above constraints with inferred role assignments, we propose modality-adaptive coordinative optimization to steer asymmetric message passing and modality-specific interaction updates. Experiments on the RAbD benchmark demonstrate that MoDA achieves state-of-the-art performance, significantly improving structural fidelity (RMSD, TM-score, lDDT) and target binding affinity.