# Immunogenicity Prediction with Dual Attention Enables Vaccine Target Selection

arXiv: https://arxiv.org/abs/2410.02647 · HTML: https://mlsanity.com/paper/2410.02647/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 55 references that are in the map.
- First posted on arXiv 3 Oct 2024.

## Builds on (most cited first, 5 of 5)

- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling](https://mlsanity.com/paper/2301.06568/) (2023)
- [Simple, Efficient and Scalable Structure-aware Adapter Boosts Protein Language Models](https://mlsanity.com/paper/2404.14850/) (2024)
- [SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering](https://mlsanity.com/paper/2301.00004/) (2022)

## Built on by (most cited first, 4 of 4)

- [VenusX: Unlocking Fine-Grained Functional Understanding of Proteins](https://mlsanity.com/paper/2505.11812/) (2025)
- [Sequence-Only Prediction of Binding Affinity Changes: A Robust and Interpretable Model for Antibody Engineering](https://mlsanity.com/paper/2505.20301/) (2025)
- [Rank-and-Reason: Multi-Agent Collaboration Accelerates Zero-Shot Protein Mutation Prediction](https://mlsanity.com/paper/2602.00197/) (2026)
- [ES-Merging: Biological MLLM Merging via Embedding Space Signals](https://mlsanity.com/paper/2603.14405/) (2026)

## Abstract (arXiv)

Immunogenicity prediction is a central topic in reverse vaccinology for finding candidate vaccines that can trigger protective immune responses. Existing approaches typically rely on highly compressed features and simple model architectures, leading to limited prediction accuracy and poor generalizability. To address these challenges, we introduce VenusVaccine, a novel deep learning solution with a dual attention mechanism that integrates pre-trained latent vector representations of protein sequences and structures. We also compile the most comprehensive immunogenicity dataset to date, encompassing over 7000 antigen sequences, structures, and immunogenicity labels from bacteria, virus, and tumor. Extensive experiments demonstrate that VenusVaccine outperforms existing methods across a wide range of evaluation metrics. Furthermore, we establish a post-hoc validation protocol to assess the practical significance of deep learning models in tackling vaccine design challenges. Our work provides an effective tool for vaccine design and sets valuable benchmarks for future research. The implementation is at https://github.com/songleee/VenusVaccine.