AbEgDiffuser: Antibody Sequence-Structure Codesign with Equivariant Graph Neural Networks and Diffusion Models

Antibodies are crucial immune proteins with high antigen specificity. Conventional antibody engineering is time-consuming and inefficient, whereas deep learning-driven specific antibody design offers an innovative avenue for drug discovery. In this work, we introduce AbEgDiffuser, a deep generative framework that enables the codesign of antibody sequences and structures conditioned on target antigens. Our model integrates diffusion models with equivariant graph neural networks and further incorporates evolutionary constraints. During forward diffusion, amino acid sequences, Cα atom coordinates, and residue orientations are progressively corrupted toward a prior distribution. In reverse, a bilevel equivariant graph neural network captures both residue- and atom-level interactions to reconstruct functional antibodies. To enforce evolutionary plausibility, we encode noisy sequences with the pretrained protein language model ESM-2. Extensive experiments on de novo antibody design and optimization tasks demonstrate that the model generates antibodies with accurate sequences and structures, as well as high binding affinity, outperforming existing design methods.