# DuaDeep-SeqAffinity: Dual-Branch Deep Learning for Tri-Stream Sequence-Based Antibody--Antigen Affinity Prediction

arXiv: https://arxiv.org/abs/2512.22007 · HTML: https://mlsanity.com/paper/2512.22007/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 58 references that are in the map.
- First posted on arXiv 26 Dec 2025.

## Builds on (most cited first, 7 of 7)

- [Conditional Antibody Design as 3D Equivariant Graph Translation](https://mlsanity.com/paper/2208.06073/) (2022)
- [Deciphering antibody affinity maturation with language models and weakly supervised learning](https://mlsanity.com/paper/2112.07782/) (2021)
- [dyAb: Flow Matching for Flexible Antibody Design with AlphaFold-driven Pre-binding Antigen](https://mlsanity.com/paper/2503.01910/) (2025)
- [AsEP: Benchmarking Deep Learning Methods for Antibody-specific Epitope Prediction](https://mlsanity.com/paper/2407.18184/) (2024)
- [Sequence-Only Prediction of Binding Affinity Changes: A Robust and Interpretable Model for Antibody Engineering](https://mlsanity.com/paper/2505.20301/) (2025)
- [AbRank: A Benchmark Dataset and Metric-Learning Framework for Antibody-Antigen Affinity Ranking](https://mlsanity.com/paper/2506.17857/) (2025)
- [VAMP-Net: An Interpretable Multi-Path Network of Genomic Permutation-Invariant Set Attention and Quality-Aware 1D-CNN for MTB Drug Resistance](https://mlsanity.com/paper/2512.21786/) (2025)

## Abstract (arXiv)

DuaDeep-SeqAffinity is a sequence-only deep learning framework that predicts antibody--antigen binding affinity directly from primary amino acid sequences, avoiding the cost and scarcity of resolved three-dimensional structures. The antigen and the antibody heavy and light chains are processed as three independent streams, each embedded with a frozen ESM-2 protein language model and passed through parallel Transformer and convolutional neural network (CNN) branches before late fusion, a decoupled design intended to preserve local complementarity-determining region (CDR) signal that monolithic encoders can dilute. On a sequence-disjoint split of the AbRank benchmark, the model achieves a Pearson correlation of 0.683, an R^2 of 0.460, and a pairwise ranking AUC of 0.895, significantly outperforming single-branch ablations (paired t-test, p < 0.05). Attention-map and gradient-based saliency analyses further show that the model preferentially attends to CDR loops and candidate epitope residues, supporting its use as a scalable, structure-free tool for high-throughput antibody screening.