AntiDIF: Accurate and Diverse Antibody Specific Inverse Folding with Discrete Diffusion

Abstract Inverse folding is an important step in current computational antibody design. Recently deep learning methods have made impressive progress in improving the sequence recovery of antibodies given their 3D backbone structure. However, inverse folding is often a one-to-many problem, i.e. there are multiple sequences that fold into the same structure. Previous methods have not taken into account the diversity between the predicted sequences for a given structure. Here we create AntiDIF an Antibody-specific discrete Diffusion model for Inverse Folding. Compared with stateof-the-art methods we show that AntiDIF improves diversity between predictions while keeping high sequence recovery rates. Furthermore, forward folding of the generated sequences shows good agreement with the target 3D structure. Competing Interest Statement The authors have declared no competing interest. Footnotes Proceedings of the Workshop on Generative AI for Biology at the 42 nd International Conference on Machine Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).