FlowDesign: Improved Design of Antibody CDRs Through Flow Matching and Better Prior Distributions
Abstract
Designing antibodies with desired binding specificity and affinity is essential for pharmaceutical research. While diffusion-based models have advanced the co-design of the Complementarity-Determining Regions (CDRs) sequences and structures, challenges remain, including non-informative prior distribution, incompatibility with discrete amino acid types, and impractical computational cost in large-scale sampling. To address these, we proposed FlowDesign, a sequence-structure co-design approach based on Flow Matching, offering: (1) Flexible selection of prior distributions; (2) Direct matching of discrete distributions; (3) Enhanced computational efficiency for large-scale sampling. By leveraging various priors, data-driven structural models proved the most informative. FlowDesign outperformed baselines in Amino Acid Recovery (AAR), RMSD, and Rosetta energy. We also applied FlowDesign to design antibodies targeting the HIV-1 receptor CD4. FlowDesign yielded antibodies with improved binding affinity and neutralizing potency compared to the antibody Ibalizumab across multiple HIV mutants, validated by Biolayer Interferometry (BLI) and pseudovirus neutralization. This highlights FlowDesign’s potential in antibody and protein design. A record of this paper’s Transparent Peer Review process is included in the Supplemental Information.
Citation:
@article{wu2025FlowDesign,
title = {FlowDesign: Improved Design of Antibody CDRs Through Flow Matching and Better Prior Distributions},
author = {Jun Wu, Xiangzhe Kong, Ningguan Sun, Jing Wei, Sisi Shan, Fuli Feng, Feng Wu, Jian Peng, View ORCID ProfileLinqi Zhang, Yang Liu, Jianzhu Ma},
journal = {Cell Systems},
year = {2025}
}
