Authors
Yunji Kim, Minkyung Baek
Published in
Bioinformatics (Oxford, England). Volume 42. Issue Supplement_2. Aug 01, 2026.
Abstract
De novo antibody design requires jointly determining the global binding orientation and shaping flexible CDR loops to engage a target epitope. Diffusion-based approaches such as RFantibody are capable of this joint task but frequently produce severe steric clashes requiring extensive post-hoc filtering. Flow-based methods such as IgFlow and FlowDesign offer more stable generation but remain restricted to pre-aligned frames, precluding true de novo design. Achieving structural integrity and epitope specificity simultaneously in this setting remains an open challenge.
We propose TiDE-Ab, a conditional SE(3) flow matching framework for de novo epitope-specific antibody design. By conditioning on unpaired antigen and antibody structures without any pre-aligned frame, TiDE-Ab inherits the structural stability of flow matching while enabling global binding pose search from scratch. To further improve epitope targeting, we introduce Time-Dependent Classifier-Free Guidance (TD-CFG), which replaces static conditioning with an adaptive schedule: strong guidance early to establish the global binding pose, followed by gradual relaxation for precise local CDR refinement. On 55 non-redundant benchmark complexes, TiDE-Ab outperforms RFantibody with higher epitope recall (0.935 vs. 0.878) and over 95% fewer steric clashes. In therapeutic case studies on TGF-β and IL-17A, TiDE-Ab reproduced the binding profiles of clinical antibodies across isoform-selective and cross-reactive epitopes, whereas RFantibody consistently failed to produce viable candidates.
Source code, data, and trained models are available at https://github.com/SNU-CSSB/TiDE-Ab.
PMID:
42635242
Bibliographic data and abstract were imported from PubMed on 24 Aug 2026.
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