EpiFormer: Learning Antigen-Antibody Interactions for Epitope Prediction via Geometric Deep Learning
Antibodies neutralize foreign antigens by binding to specific surface regions called epitopes. Computational epitope prediction is critical for understanding immune recognition and guiding antibody engineering. However, existing methods face three fundamental challenges: antibody-aware models encode each chain independently and combine them only at a late stage, failing to capture co-dependent structural features that define binding interfaces, whereas severe class imbalance and scarcity of known antibody-antigen complexes render standard training objectives ineffective. We propose EpiFormer, a general encoder-decoder framework that addresses these challenges jointly. Our key design princ...
| Authors | Mansoor Ahmed, Huirong Chai, Haoxin Wang et al. |
|---|---|
| Primary category | q-bio.QM |
| Published | 2 June 2026 |
| Last updated at source | 2 June 2026 |
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