Combining evolutionary knowledge from language models with 3D structural constraints solves vocabulary collapse in antibody design, achieving 16% better sequence accuracy and 2.3x more amino acid diversity than structure-only methods.
EvoStruct fixes a critical problem in AI-designed antibodies: neural networks trained on 3D structures alone forget important amino acid patterns from evolution. The method combines a pre-trained protein language model (which knows evolutionary patterns) with structural information, using a special adapter to merge both sources of knowledge.