# AsEP: Benchmarking Deep Learning Methods for Antibody-specific Epitope Prediction

arXiv: https://arxiv.org/abs/2407.18184 · HTML: https://mlsanity.com/paper/2407.18184/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 73 references that are in the map.
- First posted on arXiv 25 Jul 2024.

## Builds on (most cited first, 18 of 18)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [BOHB: Robust and Efficient Hyperparameter Optimization at Scale](https://mlsanity.com/paper/1807.01774/) (2018)
- [Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction](https://mlsanity.com/paper/2106.06935/) (2021)
- [A Review of Relational Machine Learning for Knowledge Graphs](https://mlsanity.com/paper/1503.00759/) (2015)
- [Inductive Relation Prediction by Subgraph Reasoning](https://mlsanity.com/paper/1911.06962/) (2019)
- [Relational Pooling for Graph Representations](https://mlsanity.com/paper/1903.02541/) (2019)
- [Deciphering antibody affinity maturation with language models and weakly supervised learning](https://mlsanity.com/paper/2112.07782/) (2021)
- [Improving Language Plasticity via Pretraining with Active Forgetting](https://mlsanity.com/paper/2307.01163/) (2023)
- [Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations](https://mlsanity.com/paper/2110.02834/) (2021)
- [Antigen-Specific Antibody Design via Direct Energy-based Preference Optimization](https://mlsanity.com/paper/2403.16576/) (2024)
- [ReFactor GNNs: Revisiting Factorisation-based Models from a Message-Passing Perspective](https://mlsanity.com/paper/2207.09980/) (2022)
- [Neural message passing for joint paratope-epitope prediction](https://mlsanity.com/paper/2106.00757/) (2021)

## Built on by (most cited first, 8 of 8)

- [CHIMERA-Bench: A Benchmark Dataset for Epitope-Specific Antibody Design](https://mlsanity.com/paper/2603.13431/) (2026)
- [AbRank: A Benchmark Dataset and Metric-Learning Framework for Antibody-Antigen Affinity Ranking](https://mlsanity.com/paper/2506.17857/) (2025)
- [EpiFormer: Learning Antigen-Antibody Interactions for Epitope Prediction via Geometric Deep Learning](https://mlsanity.com/paper/2606.04154/) (2026)
- [DuaDeep-SeqAffinity: Dual-Branch Deep Learning for Tri-Stream Sequence-Based Antibody--Antigen Affinity Prediction](https://mlsanity.com/paper/2512.22007/) (2025)
- [ConTact: Contact-First Antibody CDR Design via Explicit Interface Reasoning](https://mlsanity.com/paper/2605.21600/) (2026)
- [Deciphering Fingerprints of 3D Molecular Surfaces for Accurate Epitope Prediction](https://mlsanity.com/paper/2606.23830/) (2026)
- [Inter-Residue Geometry Attention for Antibody-Specific Epitope Prediction](https://mlsanity.com/paper/2608.01092/) (2026)
- [EpiBench: Can LLMs Understand Epitopes for Antibody Drug Discovery?](https://mlsanity.com/paper/2608.06022/) (2026)

## Abstract (arXiv)

Epitope identification is vital for antibody design yet challenging due to the inherent variability in antibodies. While many deep learning methods have been developed for general protein binding site prediction tasks, whether they work for epitope prediction remains an understudied research question. The challenge is also heightened by the lack of a consistent evaluation pipeline with sufficient dataset size and epitope diversity. We introduce a filtered antibody-antigen complex structure dataset, AsEP (Antibody-specific Epitope Prediction). AsEP is the largest of its kind and provides clustered epitope groups, allowing the community to develop and test novel epitope prediction methods and evaluate their generalisability. AsEP comes with an easy-to-use interface in Python and pre-built graph representations of each antibody-antigen complex while also supporting customizable embedding methods. Using this new dataset, we benchmark several representative general protein-binding site prediction methods and find that their performances fall short of expectations for epitope prediction. To address this, we propose a novel method, WALLE, which leverages both unstructured modeling from protein language models and structural modeling from graph neural networks. WALLE demonstrate up to 3-10X performance improvement over the baseline methods. Our empirical findings suggest that epitope prediction benefits from combining sequential features provided by language models with geometrical information from graph representations. This provides a guideline for future epitope prediction method design. In addition, we reformulate the task as bipartite link prediction, allowing convenient model performance attribution and interpretability. We open source our data and code at https://github.com/biochunan/AsEP-dataset.