# Multi-Modality Representation Learning for Antibody-Antigen Interactions Prediction

arXiv: https://arxiv.org/abs/2503.17666 · HTML: https://mlsanity.com/paper/2503.17666/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 39 references that are in the map.
- First posted on arXiv 22 Mar 2025.

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

- [Directional Message Passing for Molecular Graphs](https://mlsanity.com/paper/2003.03123/) (2020)
- [Learning from Protein Structure with Geometric Vector Perceptrons](https://mlsanity.com/paper/2009.01411/) (2020)
- [ATOM3D: Tasks On Molecules in Three Dimensions](https://mlsanity.com/paper/2012.04035/) (2020)
- [Learning Hierarchical Protein Representations via Complete 3D Graph Networks](https://mlsanity.com/paper/2207.12600/) (2022)
- [Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equation](https://mlsanity.com/paper/2301.10814/) (2023)
- [Progressive Multi-Modality Learning for Inverse Protein Folding](https://mlsanity.com/paper/2312.06297/) (2023)
- [AVIDa-hIL6: A Large-Scale VHH Dataset Produced from an Immunized Alpaca for Predicting Antigen-Antibody Interactions](https://mlsanity.com/paper/2306.03329/) (2023)
- [ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion](https://mlsanity.com/paper/2412.19589/) (2024)

## Built on by (most cited first, 2 of 2)

- [Uncertainty-Aware Metabolic Stability Prediction with Dual-View Contrastive Learning](https://mlsanity.com/paper/2506.00936/) (2025)
- [HiF-DTA: Hierarchical Feature Learning Network for Drug-Target Affinity Prediction](https://mlsanity.com/paper/2510.27281/) (2025)

## Abstract (arXiv)

While deep learning models play a crucial role in predicting antibody-antigen interactions (AAI), the scarcity of publicly available sequence-structure pairings constrains their generalization. Current AAI methods often focus on residue-level static details, overlooking fine-grained structural representations of antibodies and their inter-antibody similarities. To tackle this challenge, we introduce a multi-modality representation approach that integates 3D structural and 1D sequence data to unravel intricate intra-antibody hierarchical relationships. By harnessing these representations, we present MuLAAIP, an AAI prediction framework that utilizes graph attention networks to illuminate graph-level structural features and normalized adaptive graph convolution networks to capture inter-antibody sequence associations. Furthermore, we have curated an AAI benchmark dataset comprising both structural and sequence information along with interaction labels. Through extensive experiments on this benchmark, our results demonstrate that MuLAAIP outperforms current state-of-the-art methods in terms of predictive performance. The implementation code and dataset are publicly available at https://github.com/trashTian/MuLAAIP for reproducibility.