AgForce Enables Antigen-conditioned Generative Antibody Design

Abstract

Antibody design methods condition on antigen structure to generate complementarity-determining regions (CDR), yet a systematic evaluation of baseline methods reveals that they largely ignore the antigen input. We identify three failure modes that explain this behavior. Antigen blindness arises because models derive predictions from antibody framework context rather than antigen information, producing nearly identical CDRs regardless of the target. Vocabulary collapse reduces predicted amino acids to three to five per position, far below the ground truth distribution in native sequences. The argmax bottleneck on the cross-entropy loss via greedy decoding acts as a null predictor that sees only position and loop length and recovers almost all the residues irrespective of the model architecture and the target antigen. We propose a novel encoder-decoder architecture called AgForce that uses a graph neural network (GNN) as the encoder and specialized decoders for sequence-structure co-design. Specifically, we apply framework dropout, gated bottlenecks, and hyperbolic cross attention that prevent the antibody shortcut path. In the decoder, a Mixture Density Network (MDN) sequence head with Potts-like pairwise coupling and annealed Multiple Choice Learning (aMCL) replaces the cross-entropy objective with a multi-component distribution. An antigen cycle consistency head routes gradients through the sequence decoder, forcing predicted distributions to encode antigen identity. On the ChiMERa-Bench dataset, AgForce leads the baselines on every interface metric on all three splits (fnat, iRMSD, DockQ, and epitope F1) together with structure and sequence, while achieving roughly a 2x larger effective vocabulary compared to the GNN baselines.