AbGate: Length-Decoupled Antigen-Conditioned Antibody Design

Antibodies are critical molecular therapeutics, but a new target is usually available only as a sequence, without the solved structure or mapped epitope that existing generators need to run. We propose AbGate, which conditions a frozen antibody language model on the antigen sequence alone. A learned pooler compresses the antigen into a fixed number of tokens whatever its length and a cross-attention module then injects these tokens into every decoder layer, so antigen length never changes how much of the decoder's context AbGate uses. Training only 1.09% of its parameters, AbGate performs on par with MAGE, a fully fine-tuned generator trained on the same corpus, across every structural metric we measure on a shared target. Across 502 held-out targets AbGate generates antibodies at 95.6% filter retention and 100% novelty against the training pairs, and they carry the 9-mer composition of natural human repertoires. Additionally, on an independent test set of 496 targets, antibodies generated by AbGate performed comparably to experimentally validated binding antibodies on interface confidence.