# Neural message passing for joint paratope-epitope prediction

arXiv: https://arxiv.org/abs/2106.00757 · HTML: https://mlsanity.com/paper/2106.00757/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 31 May 2021.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [DropEdge: Towards Deep Graph Convolutional Networks on Node Classification](https://mlsanity.com/paper/1907.10903/) (2019)
- [Neural Machine Translation in Linear Time](https://mlsanity.com/paper/1610.10099/) (2016)
- [Attentive cross-modal paratope prediction](https://mlsanity.com/paper/1806.04398/) (2018)

## Built on by (most cited first, 10 of 10)

- [AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Optimisation](https://mlsanity.com/paper/2201.12570/) (2022)
- [Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement](https://mlsanity.com/paper/2207.06616/) (2022)
- [AsEP: Benchmarking Deep Learning Methods for Antibody-specific Epitope Prediction](https://mlsanity.com/paper/2407.18184/) (2024)
- [Improving Paratope and Epitope Prediction by Multi-Modal Contrastive Learning and Interaction Informativeness Estimation](https://mlsanity.com/paper/2405.20668/) (2024)
- [ImmunoLingo: Linguistics-based formalization of the antibody language](https://mlsanity.com/paper/2209.12635/) (2022)
- [Graph Representation Learning for Interactive Biomolecule Systems](https://mlsanity.com/paper/2304.02656/) (2023)
- [Geometric Epitope and Paratope Prediction](https://mlsanity.com/paper/2307.13608/) (2023)
- [ABConformer: Physics-inspired Sliding Attention for Antibody-Antigen Interface Prediction](https://mlsanity.com/paper/2509.23254/) (2025)
- [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)

## Abstract (arXiv)

Antibodies are proteins in the immune system which bind to antigens to detect and neutralise them. The binding sites in an antibody-antigen interaction are known as the paratope and epitope, respectively, and the prediction of these regions is key to vaccine and synthetic antibody development. Contrary to prior art, we argue that paratope and epitope predictors require asymmetric treatment, and propose distinct neural message passing architectures that are geared towards the specific aspects of paratope and epitope prediction, respectively. We obtain significant improvements on both tasks, setting the new state-of-the-art and recovering favourable qualitative predictions on antigens of relevance to COVID-19.