RAFA: Reward-Aligned Flow Tuning for Antibody and Nanobody Design

We introduce RAFA, an antigen-conditioned framework that jointly generates CDR sequences and the complete antibody structure from an antigen structure, epitope information, and an antibody framework sequence, without requiring initial antibody coordinates. Antibody and nanobody design requires coordinating sequence, structure, and the interface with a specified antigen. Generative models trained to reconstruct observed complexes do not necessarily optimize the quality of their generated interfaces. Building on La-Proteina's partially latent autoencoder, the model combines explicit $C_{\alpha }$ coordinates with per-residue latents encoding sequence and side-chain geometry. Joint flow matching is complemented by geometry supervision. We further introduce Reward-Gradient Transport (RGT), which approximately transports terminal reward gradients with respect to antibody $C_{\alpha }$ coordinates to recorded states along the same sampled trajectory and updates the shared generative velocity field. Relative to RAFA, RAFA (RL) improves mean DockQ by 22.0% for antibody design and 28.9% for nanobody design on a globally filtered, held-out benchmark. Additional structure-prediction, refolding-confidence, and interface analyses characterize both improvements and trade-offs. These results support coordinate reward-gradient post-training as an approach to improving computational antibody–antigen interface generation.